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<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: A chemical dynamic model for the infiltration of outdoor size-resolved ammonium nitrate aerosols to indoor environments. In the present study, we developed a chemical dynamic model to describe the infiltration of size-resolved ammonium nitrate aerosols from outdoor to indoor environments. This model considered the penetration factor, deposition rate, and the reversible reaction process, which was quantified by the diffusive molar flux on the surface of ammonium nitrate aerosols depending on indoor temperature, humidity, and concentrations of nitric acid (HNO3 ) and ammonia (NH3 ). To verify the model, we employed a single-particle aerosol mass spectrometer with an automated switching system to simultaneously measure size-resolved outdoor and indoor ammonium nitrate aerosols. Comparisons between the predicted and measured concentrations of these aerosols showed a mean relative error of 4.8 +/- 18.3%. To analyze the sensitivity of model parameters, several parameters were perturbed. This analysis indicated that parameters related to HNO3 were more sensitive than those related to NH3 because the indoor gas phase concentration of NH3 was much higher than that of HNO3 . <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 71 96 ammonium nitrate aerosols T2 Environmental_exposures 228 253 ammonium nitrate aerosols T3 Environmental_exposures 458 483 ammonium nitrate aerosols T4 Environmental_exposures 760 785 ammonium nitrate aerosols T6 Environmental_exposures 858 866 aerosols <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: SCAN: a method of community collaboration. Recent research has highlighted the complexity of the battered child syndrome. Clinical experience suggests that treatment of troubled families is a long-term process, draining financial resources of agencies and emotional resources of professionals. The need to provide support and case consultation to professionals working in the area of child abuse and neglect evolved in the establishment, in 1968, of the Suspected Child Abuse and Neglect (SCAN) Program at Children's Hospital, Pittsburgh, Pennsylvania. This program provides an effective method of intervention through identification, consultation, referral and follow-up of at-risk families. Weekly meetings offer multi-disciplinary case coordination, treatment recommendations, and a channel of communication for representatives of community agencies. This paper traces the history and present status of the SCAN Program, as well as the role of SCAN as a force for change of community attitudes. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 170 187 troubled families T2 Socioeconomic_factors 385 396 child abuse T6 Socioeconomic_factors 385 390;401 408 child neglect <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Effects of high-AGE beverage on RAGE and VEGF expressions in the liver and kidneys. BACKGROUND: The formation and accumulation of advanced glycation end products (AGEs) increase in some lifestyle-related diseases as well as in aging; however, little is known about the relationship between food-derived AGEs and the pathology of such diseases. AIM OF THE STUDY AND METHODS: To explore whether food items containing high levels of AGEs are involved in the development of lifestyle-related diseases, rats were orally administered a commercial high-AGE beverage [Lactobacillus beverage-A (LB-A)]. With a particular focus on angiogenesis-associated diseases, the gene expressions of vascular endothelial growth factor (VEGF) and the receptor for AGEs (RAGE) were examined in the liver and kidneys using real-time reverse transcription-polymerase chain reaction. Moreover, AGE deposition was immunohistochemically investigated in these tissues. RESULTS AND CONCLUSIONS: Hepatic VEGF expression was significantly increased in rats administered LB-A (P < 0.01 vs. control). Furthermore, immunohistochemical analysis detected glucose-derived AGE-positive cells in the liver from the LB-A group. These results suggest that AGE-rich beverages increase hepatic VEGF expression and AGE accumulation, bringing about early events associated with lifestyle-related diseases. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 20 28 beverage T2 Nutrition 551 559 beverage T11 Nutrition 1224 1233 beverages T8 Nutrition 291 295 food T9 Nutrition 394 398 food <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Postprandial Metabolic Effects of Accelerometer Measured Spontaneous Low-Level Activity. BACKGROUND: Interrupting sedentary time induces improvements in glucose metabolism; however, it is unclear how much activity is required to reduce the negative effects of prolonged sitting. METHODS: Sixty-six participants sat continuously for 9 hours except for required bathroom breaks. Participants were fed meal replacement beverages at 60, 240 and 420 min. Blood samples were obtained hourly for 9 hours, with additional samples collected 30 and 45 min after each feeding. Responses were calculated as incremental area under the curve (iAUC) for plasma glucose, insulin and triglyceride. Participants wore a triaxial accelerometer and a heart rate monitor. Energy expenditure was estimated using indirect calorimetry. RESULTS: After controlling for age, sex and BMI, every 100 count increase in accelerometer derived total movement was associated with a 0.06 mmol.L-1.9 hours decrease in glucose iAUC (95% CI 0.004-0.1; P = .035), but not associated with changes in insulin or triglyceride iAUC. Every 1 bpm increase in mean heart rate was associated with a 0.76 mmol.L-1.9 hours increase in triglyceride iAUC (95% CI 0.13-1.38). CONCLUSION: Accelerometer measured movement during periods of prolonged sitting can result in minor improvements in postprandial glucose metabolism, but not lipid metabolism. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 261 278 prolonged sitting T2 Nutrition 400 426 meal replacement beverages T3 Physical_activity 1286 1303 prolonged sitting T4 Physical_activity 115 129 sedentary time T6 Physical_activity 206 214 activity T8 Nutrition 751 769 Energy expenditure T12 Physical_activity 69 87 Low-Level Activity <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: A recurrent central giant cell granuloma in a young patient and orthodontic treatment: a case report. Central giant cell granuloma (CGCG) is an uncommon benign intraosseous lesion of the jaw, found predominantly in children and young adults below 30 years of age. The purpose of this article was to present a summary of the current literature and a case report of an 11-year-old boy diagnosed with an aggressive CGCG involving the anterior maxilla that was removed in 2004 and subsequently recurred almost 3 years later in 2006. The presenting features of the patient and the effect of combined surgical and orthodontic treatment for this condition are discussed. This case shows how the dentition was successfully maintained with conservative surgery and orthodontic treatment in spite of the extensive destruction of the supporting bone, and the importance of long-term follow-up. The report also reminds orthodontic practitioners that rare pathological conditions can occur in their child patient groups. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Beauty_and_Cleaning 64 85 orthodontic treatment T2 Beauty_and_Cleaning 609 630 orthodontic treatment T4 Beauty_and_Cleaning 757 778 orthodontic treatment <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Factors affecting exercise attendance and completion in sedentary older adults: a meta-analytic approach. BACKGROUND: Many different constructs are used currently in the literature to assess exercise adherence. This study examined whether the same or different variables predict exercise attendance and exercise completion among sedentary older adults. METHODS: Thirty-seven randomized control trials were selected from articles published between 1980 and 2000 that tested exercise interventions for sedentary older adults. Block-entry, weighted, hierarchical meta-regression analyses were conducted. RESULTS: Different factors predicted attendance and completion. Group-based (P < .05) and resistance exercise (P < .1) predicted higher attendance rates than individual-based and aerobic exercise. In contrast, facility-based exercise was associated with higher completion rates than homebased exercise (P < .1). CONCLUSIONS: Results show that completing a program is not synonymous with good attendance. Program designers need to consider different strategies to boost both of these rates that need to be maximized to best benefit program participants. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 56 78 sedentary older adults T2 Socioeconomic_factors 330 352 sedentary older adults T3 Socioeconomic_factors 501 523 sedentary older adults T6 Physical_activity 192 200 exercise T8 Physical_activity 474 496 exercise interventions T9 Physical_activity 692 711 resistance exercise T10 Physical_activity 781 797 aerobic exercise T11 Physical_activity 812 835 facility-based exercise T12 Physical_activity 885 903 homebased exercise T5 Physical_activity 280 288 exercise T4 Physical_activity 18 26 exercise T7 Physical_activity 304 312 exercise <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Physical and sexual abuse in runaway youths. This paper examines the extent of physical and sexual abuse among runaway youths and the association of that abuse with behavior, mental health, life events, and parental mental health. Of 291 youths who sought shelter at homes for runaway youths, almost half (141) reported a history of physical or sexual abuse. The results emphasize the need that these abused youths and their parents have for mental health services: One in five of the nonabused youths and one in three of the abused youths endorsed parental descriptions reflective of antisocial personality and/or drug problems, and runaways who were abused had a mean level of behavior problems in the clinically significant area. Multivariate analyses demonstrate that the simple existence of physical or sexual abuse impacts on self-esteem and overall behavior problems regardless of other family problems. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 0 8;20 25 Physical abuse T2 Socioeconomic_factors 93 105 sexual abuse T3 Socioeconomic_factors 346 358 sexual abuse T4 Drugs 616 629 drug problems T5 Socioeconomic_factors 809 821 sexual abuse T6 Socioeconomic_factors 895 910 family problems T7 Socioeconomic_factors 155 160 abuse T8 Socioeconomic_factors 13 25 sexual abuse T9 Socioeconomic_factors 100 105;80 88 abuse physical T10 Socioeconomic_factors 334 342;353 358 physical abuse T11 Socioeconomic_factors 797 805;816 821 physical abuse <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Understanding the Relationship Between Pet Ownership and Physical Activity Among Older Community-Dwelling Adults-A Mixed Methods Study. Pet ownership is associated with increased levels of physical activity (PA) in older adults. Studies have mainly focused on the association between PA and dog walking; however, broader aspects of pet ownership may influence PA. The purpose of this study was to explore the association between pet ownership and incidental and purposeful PA using a mixed methods approach. Participants' (N = 15) PA was measured for 7 days using accelerometers and diaries. Semistructured interviews explored participants' perspectives regarding pet-related activities. Participants' mean (SD) daily step count was 14,204 (5,061) steps, and mean (SD) sedentary time per day was 8.76 (1.18) hr. Participants strongly concurred that their pets were an integral part of their daily lives. Incidental and purposeful PA resulted from participants undertaking pet care and socially interacting with their pets. Pets may interrupt sedentary behaviors by nudging older adults to engage in PA as part of their daily lived experience. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 57 74 Physical Activity T4 Physical_activity 361 363 PA T5 Physical_activity 1100 1102 PA T6 Physical_activity 931 933 PA T7 Physical_activity 474 476 PA T8 Physical_activity 285 287 PA T9 Non_physical_leisure_time_activities 39 52 Pet Ownership T10 Non_physical_leisure_time_activities 137 150 Pet ownership T11 Non_physical_leisure_time_activities 430 443 pet ownership T12 Non_physical_leisure_time_activities 333 346 pet ownership T14 Physical_activity 532 534 PA T15 Physical_activity 665 687 pet-related activities T17 Physical_activity 770 784 sedentary time T18 Non_physical_leisure_time_activities 986 1022 socially interacting with their pets T19 Physical_activity 1043 1062 sedentary behaviors T2 Physical_activity 190 207 physical activity T3 Physical_activity 292 303 dog walking T13 Physical_activity 209 211 PA T20 Non_physical_leisure_time_activities 973 981 pet care <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Hypodontia, Oligodontia and Anodontia in West Virginia Appalachia. Purpose: Children in West Virginia have a high prevalence of missing permanent teeth when compared to children in the rest of the nation. The purpose of this study is to determine the prevalence of permanent tooth hypodontia/oligodontia/anodontia in West Virginia children and to compare the prevalence by sex.Methods: Five hundred panoramic radiographs of West Virginia children, ages 6-11 years, were examined for missing permanent tooth buds/permanent teeth. Data analyses included frequency determinations, Chi square analyses, and logistic regression.Results: Sixty children (12.0%, n=500) had at least one missing permanent tooth bud/permanent tooth. There were 15.5% of females and 8.8% of males who had at least one missing permanent tooth bud/permanent tooth. In adjusted logistic regression on at least one missing permanent tooth bud/permanent tooth, females had an adjusted odds ratio of 2.11 [95% Confidence Interval: 1.18, 3.75; p = .011] compared with males. Other variables in the analysis failed to reach significance.Conclusion: In this sample of West Virginia children, females were more likely to have at least one missing permanent tooth bud/permanent tooth compared to males. Early recognition and treatment planning for dental care is needed for children with hypodontia/oligodontia/anodontia. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T6 Beauty_and_Cleaning 1311 1322 dental care <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Alternate rapid maxillary expansion-constriction and tooth-borne symphyseal distraction osteogenesis : A case report demonstrating treatment of a patient with severe crowding. Surgically assisted rapid mandibular expansion is a contemporary treatment alternative to enable genuine skeletal mandibular widening. Mandibular widening via a tooth-borne distractor is a practical and noninvasive clinical approach. Recently, to expand and protract the maxilla, the alternate rapid maxillary expansion-and-constriction procedure was suggested. In this case report, we describe a female patient (12 years 7 months old) having severe maxillary and mandibular crowding who underwent repeated alternate rapid maxillary expansion and constriction in combination with tooth-borne symphyseal distraction osteogenesis. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Inhalation transfer factors for air pollution health risk assessment. To facilitate routine health risk assessments, we develop the concept of an inhalation transfer factor (ITF). The ITF is defined as the pollutant mass inhaled by an exposed individual per unit pollutant mass emitted from an air pollution source. A cumulative population inhalation transfer factor (PITF) is also defined to describe the total fraction of an emitted pollutant inhaled by all members of the exposed population. In this paper, ITFs and PITFs are calculated for outdoor releases from area, point, and line sources, indoor releases in single zone and multizone indoor environments, and releases within motor vehicles. Typical PITFs for an urban area from emissions outdoors are approximately 10(-6)-10(-3). PITFs associated with emissions in buildings or in moving vehicles are typically much higher, approximately 10(-3)-10(-1). <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 32 45 air pollution T4 Environmental_exposures 295 315 air pollution source T7 Environmental_exposures 668 698 releases within motor vehicles T9 Socioeconomic_factors 721 731 urban area T5 Environmental_exposures 428 445 emitted pollutant T3 Environmental_exposures 207 216 pollutant T6 Environmental_exposures 264 273 pollutant T8 Environmental_exposures 737 755 emissions outdoors T10 Environmental_exposures 811 833 emissions in buildings T11 Environmental_exposures 811 820;837 855 emissions in moving vehicles <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The relationship between young adults' beliefs about anonymity and subsequent cyber aggression. Anonymity is considered a key motivator for cyber aggression, but few investigations have focused on the connection between anonymity and the subsequent engagement in aggression through the cyber context. The present longitudinal study utilized structural equation modeling to reveal indirect associations between two types of anonymity (i.e., punishment by authority figures and retaliation from the target) and later cyber aggression among 130 young adults. These relationships were examined through the influence of beliefs about not getting caught and not believing in the permanency of online content. Findings indicated that both forms of anonymity were related to cyber aggression 6 months later through two explanatory mechanisms (i.e., confidence with not getting caught and believing online content is not permanent), after controlling for gender and cyber aggression at Time 1. The implications of these findings are discussed, and an appeal for additional research investigating cyber aggression among young adults is given. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 78 94 cyber aggression T2 Socioeconomic_factors 141 157 cyber aggression T3 Socioeconomic_factors 768 784 cyber aggression T4 Socioeconomic_factors 1088 1104 cyber aggression T5 Socioeconomic_factors 516 532 cyber aggression T6 Socioeconomic_factors 958 974 cyber aggression T7 Socioeconomic_factors 264 274 aggression <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Day-to-day variability of physical activity of older adults living in the community. The purpose of the study was to investigate whether a 24-hr recording period is sufficient to describe physical activity (PA) of 1 week for intraindividual comparison in older adults. Furthermore, the authors analyzed whether physical performance can be used as a surrogate marker of PA. PA was captured on 7 consecutive days by a body-fixed sensor in 44 community-dwelling older adults (80.75 +/- 4.05 yr). Mean times of walking and of "time on feet" of the group were 10.2 hr (+/- 3.5) and 35.1 hr (+/- 9.43), respectively. Intraindividual variabilities of walking and of time on feet were 31.9% +/- 10.79% and 19.4% +/- 8.76%, respectively. Accumulated time of variables of PA showed no differences between weekdays, with variabilities of 3.8% and 1.8% for walking and time on feet, respectively. Association between Short Physical Performance Battery and PA was limited (walking r = .397, time on feet r = .41). <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 26 43 physical activity T4 Physical_activity 370 372 PA T5 Physical_activity 763 765 PA T6 Physical_activity 945 947 PA T7 Physical_activity 374 376 PA T2 Physical_activity 189 206 physical activity T3 Physical_activity 846 853 walking T8 Physical_activity 508 515 walking T9 Physical_activity 645 652 walking T10 Physical_activity 961 968 walking T11 Physical_activity 208 210 PA T12 Physical_activity 524 536 time on feet T13 Physical_activity 858 870 time on feet T15 Physical_activity 660 672 time on feet T14 Physical_activity 979 991 time on feet <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Factors associated with exercise counseling and program preferences among breast cancer survivors. OBJECTIVE: Our aim was to assess differences in exercise counseling preferences, program preferences, and telephone/Internet access among breast cancer survivors based on exercise behavior and demographic, medical, social cognitive, and environmental factors. METHODS: A self-administered survey was returned by 192 breast cancer survivors. RESULTS: Participants were Caucasian (98%), and the mean age was 64+/-11.5 years. Participants preferring an exercise specialist were more likely to report current treatment, higher self-efficacy, greater perceived barriers, and a residential environment conducive to physical activity. Participants preferring face-to-face counseling and exercising outdoors were younger, and those preferring to exercise alone and at home reported lower social support. Low-intensity exercise was preferred by participants who were sedentary, obese, less self-efficacious, enjoyed exercise less, perceived greater barriers, and reported lower social support. Participants with Internet access were more apt to be younger with higher income and greater social support. CONCLUSIONS: Demographic, medical, social cognitive, and environmental factors might influence exercise preferences and Internet access. Future research assessing the effectiveness of tailoring interventions based on these factors is warranted. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 24 43 exercise counseling T2 Physical_activity 148 167 exercise counseling T4 Physical_activity 271 288 exercise behavior T6 Environmental_exposures 337 358 environmental factors T8 Socioeconomic_factors 672 695 residential environment T9 Physical_activity 709 726 physical activity T10 Mental_health_practices 752 775 face-to-face counseling T11 Physical_activity 780 799 exercising outdoors T12 Physical_activity 838 852 exercise alone T13 Socioeconomic_factors 880 894 social support T14 Physical_activity 896 918 Low-intensity exercise T15 Physical_activity 1007 1015 exercise T16 Socioeconomic_factors 1069 1083 social support T18 Socioeconomic_factors 1152 1165 higher income T19 Socioeconomic_factors 1170 1192 greater social support T20 Socioeconomic_factors 1229 1245;1265 1272 social cognitive factors T21 Environmental_exposures 1251 1272 environmental factors T22 Physical_activity 1289 1309 exercise preferences T5 Socioeconomic_factors 315 331;351 358 social cognitive factors T25 Physical_activity 838 846;857 864 exercise at home T26 Physical_activity 958 967 sedentary <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Phenolic composition, antioxidant and enzyme inhibitory activities of Eryngium bornmuelleri leaf. Eryngium bornmuelleri Nab. (Tusi) is an endemic botanical from the Eastern Anatolia region of Turkey traditionally used for preparation of herbal tea. Within this study, phenolic composition, antioxidant capacities and inhibitory activities towards selected digestive enzymes of E. bornmuelleri leaf were investigated. Sequential extracts, obtained by extraction of plant tissue by ethanol, acetone and water exhibited pronounced antioxidant capacities and in a dose-dependent manner suppressed the metabolic syndrome related enzymes: alpha-amylase, alpha-glucosidase and pancreatic lipase. All extracts contained high levels of phenolic compounds. Flavonoid glycosides were the main phytochemicals detected, with rutin as the major compound (70% of total phenolics). Chlorogenic, hydroxybenzoic and caftaric acids as well as traces of caffeic, ferulic and rosmarinic acids were also detected. Correlation analysis indicated that phenolic compounds were the major sources of the enzyme-inhibitory activities. This study suggests that E. bornmuelleri leaf extracts can modulate the metabolism of sugars and fats through inhibition of the relevant digestive enzymes. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 783 797 phytochemicals T3 Nutrition 238 248 herbal tea T4 Nutrition 748 768 Flavonoid glycosides T5 Nutrition 1149 1162 leaf extracts T6 Nutrition 1194 1200 sugars T7 Nutrition 1029 1047 phenolic compounds T9 Nutrition 291 313 antioxidant capacities T10 Nutrition 529 551 antioxidant capacities T11 Nutrition 728 746 phenolic compounds T13 Nutrition 0 20 Phenolic composition T14 Nutrition 92 96 leaf T15 Nutrition 269 289 phenolic composition T16 Nutrition 394 398 leaf T17 Nutrition 813 818 rutin T18 Nutrition 855 864 phenolics T19 Nutrition 867 878;908 913 Chlorogenic acids T20 Nutrition 880 894;908 913 hydroxybenzoic acids T21 Nutrition 899 913 caftaric acids T22 Nutrition 935 942;967 972 caffeic acids T23 Nutrition 944 951;967 972 ferulic acids T24 Nutrition 956 972 rosmarinic acids T25 Nutrition 1205 1209 fats <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Who are the long sleepers? Towards an understanding of the mortality relationship. While much is known about the negative health implications of insufficient sleep, relatively little is known about risks associated with excessive sleep. However, epidemiological studies have repeatedly found a mortality risk associated with reported habitual long sleep. This paper will summarize and describe the numerous studies demonstrating increased mortality risk associated with long sleep. Although these studies establish a mortality link, they do not sufficiently explain why such a relationship might occur. Possible mechanisms for this relationship will be proposed and described, including (1) sleep fragmentation, (2) fatigue, (3) immune function, (4) photoperiodic abnormalities, (5) lack of challenge, (6) depression, or (7) underlying disease process such as (a) sleep apnea, (b) heart disease, or (c) failing health. Following this, we will take a step back and carefully consider all of the historical and current literature regarding long sleep, to determine whether the scientific evidence supports these proposed mechanisms and ascertain what future research directions may clarify or test these hypotheses regarding the relationship between long sleep and mortality. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Sleep 12 25 long sleepers T2 Sleep 146 164 insufficient sleep T3 Sleep 221 236 excessive sleep T4 Sleep 335 354 habitual long sleep T5 Sleep 471 481 long sleep T6 Sleep 692 711 sleep fragmentation T8 Sleep 1039 1049 long sleep T9 Sleep 1249 1259 long sleep T10 Socioeconomic_factors 784 801 lack of challenge <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The effects of signage and the physical environment on stair usage. BACKGROUND: Physical activity (PA) is a health behavior that most Americans do not participate in at recommended levels. METHODS: We sought to increase PA by use of motivational signs in selected buildings. Because physical environments are known to influence PA, the relationship of building characteristics and stair usage was also assessed. One pre- and two post-intervention observations were conducted. RESULTS: The proportion of those using the stairs increased from baseline to the second data collection, (chi2 = 39.31, p < 0.01) and baseline to a final data collection (chi2 = 10.1, p < 0.01). Built environment factors, including steps to the next higher floor and the number of floors in the building were consistent predictors of stair use. With signs positioned, the visibility of the stairs while standing in front of elevators became a significant predictor of stair usage. CONCLUSIONS: Motivational signs and characteristics of built environments are associated with increased stair usage. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 31 51 physical environment T2 Physical_activity 81 98 Physical activity T3 Physical_activity 221 223 PA T4 Physical_activity 329 331 PA T5 Environmental_exposures 284 305 physical environments T6 Physical_activity 100 102 PA <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The DONALD Study. History, current status and future perspectives. Nutrition during childhood and adolescence is an important determinant of development and health, both for the child and the later adult. In industrialised countries as well as in many countries of economic transition, emphasis has moved from combating nutrient deficiencies to research on the effects of overnutrition and food selection. Prevention of chronic diseases including obesity have become a major focus in research. However, the complex relation between infant growth and its related endocrine and metabolic changes on the one hand and the influence of nutrition and nutritional status on the other hand still need to be understood in detail. Studies aiming to elucidate this have to follow children and adolescents during their growth period. The following pages display the features of the German DONALD Study ( DOrtmund Nutritional and Anthropometric Longitudinally Designed Study) which was specifically designed to address these complex research questions. Finally, comparisons to other studies are made and the specific strength and weaknesses of this study are discussed. As the DONALD study offers unique research opportunities and due to its long follow-up an abundance of data, collaborative research is encouraged. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 68 77 Nutrition T2 Nutrition 632 641 nutrition T3 Socioeconomic_factors 209 233 industrialised countries T4 Nutrition 646 664 nutritional status T6 Nutrition 373 386 overnutrition T7 Nutrition 391 405 food selection T8 Socioeconomic_factors 253 285 countries of economic transition T5 Nutrition 321 342 nutrient deficiencies <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Classification of maltreatment-related mortality by Child Death Review teams: How reliable are they? Accurate estimation of the incidence of maltreatment-related child mortality depends on reliable child fatality review. We examined the inter-rater reliability of maltreatment designation for two Alaskan Child Death Review (CDR) panels. Two different multidisciplinary CDR panels each reviewed a series of 101 infant and child deaths (ages 0-4 years) in Alaska. Both panels independently reviewed identical medical, autopsy, law enforcement, child welfare, and administrative records for each death utilizing the same maltreatment criteria. Percent agreement for maltreatment was 64.7% with a weighted Kappa of 0.61 (95% CI 0.51, 0.70). Across maltreatment subtypes, agreement was highest for abuse (69.3%) and lowest for negligence (60.4%). Discordance was higher if the mother was unmarried or a smoker, if residence was rural, or if there was a family history of child protective services report(s). Incidence estimates did not depend on which panel's data were used. There is substantial room for improvement in the reliability of CDR panel assessment of maltreatment related mortality. Standardized decision guidance for CDR panels may improve the reliability of their data. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 18 30 maltreatment T2 Socioeconomic_factors 142 154 maltreatment T3 Socioeconomic_factors 265 277 maltreatment T4 Socioeconomic_factors 665 677 maltreatment T5 Socioeconomic_factors 746 758 maltreatment T6 Socioeconomic_factors 1161 1173 maltreatment T7 Socioeconomic_factors 620 632 maltreatment T8 Socioeconomic_factors 544 557 child welfare T10 Socioeconomic_factors 824 834 negligence T11 Socioeconomic_factors 885 894 unmarried T9 Socioeconomic_factors 795 800 abuse <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Nicotine and 3-ethenylpyridine concentrations as markers for environmental tobacco smoke in restaurants. The Finnish Tobacco Act has restricted smoking in public places since 1976, and in 1994 the Act was amended to include workplaces as well. In 2000, the Tobacco Act will be expanded further to restaurants. In Finland, the exposure of clients and employees to the vapor phase environmental tobacco smoke (ETS) in restaurants has not been systematically studied before by measuring ETS markers in indoor air. However, in these establishments the concentrations of ETS are expected to be much higher than in other workplaces. Gaseous nicotine and 3-ethenylpyridine were used as indicators of ETS in three different types of restaurants. Mean concentrations of nicotine ranged from 1.4-42.2 micrograms/m3 and 3-ethenylpyridine 1.4-6.3 micrograms/m3. In addition, concentrations of total volatile organic compounds (TVOC), CO and CO2 were measured and concentrations were 183-2215 micrograms/m3, 0.9-3.1 mg/m3 and 600-880 ppm, respectively. The concentrations of ETS markers were highest in discos and nightclubs and lowest in restaurants. The concentrations of total volatile organic compounds were highest in discos and nightclubs, especially when smoke generators were used. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Drugs 0 8 Nicotine T5 Environmental_exposures 649 666 3-ethenylpyridine T6 Environmental_exposures 810 827 3-ethenylpyridine T7 Drugs 61 88 environmental tobacco smoke T10 Drugs 485 488 ETS T11 Drugs 1063 1066 ETS T12 Drugs 694 697 ETS T15 Drugs 628 644 Gaseous nicotine T19 Environmental_exposures 1250 1266 smoke generators T20 Environmental_exposures 1162 1194 total volatile organic compounds T22 Environmental_exposures 916 920 TVOC T23 Environmental_exposures 923 925 CO T24 Environmental_exposures 930 933 CO2 T28 Socioeconomic_factors 225 235 workplaces T29 Socioeconomic_factors 616 626 workplaces T21 Environmental_exposures 882 914 total volatile organic compounds T13 Drugs 567 570 ETS T3 Drugs 762 770 nicotine T4 Drugs 13 30 3-ethenylpyridine T27 Drugs 145 169 smoking in public places T8 Drugs 380 407 environmental tobacco smoke T9 Drugs 409 412 ETS <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Extended regimens of combined hormonal contraception to reduce symptoms related to withdrawal bleeding and the hormone-free interval: a systematic review of randomised and observational studies. OBJECTIVE: To assess whether continuous and extended regimens (CRs/ERs) of combined hormonal contraceptives (CHCs) improve symptoms related to withdrawal bleeding or the hormone-free interval and to compare the efficacy, safety, and cost of CRs/ERs to those of conventional 28-day regimens. STUDY DESIGN: A literature search of the PubMed database was conducted for randomised clinical trials (RCTs) and observational studies published in any language between 2006 and 2013. RESULTS: Sixteen RCTs and 14 observational studies evaluated issues related to our objectives. CRs/ERs, whose efficacy and safety were comparable to those described for conventional regimens, were preferred due to their improvement of symptoms related to withdrawal bleeding or the hormone-free interval and the lower costs resulting from the reduced incidence of these symptoms. CONCLUSION: The contraceptive efficacy and safety of CR/ER use of CHCs is at least equal to that of 28-days conventional regimens, and this use may have some cost savings. CRs/ERs are recommended for women willing to take a CHC for treatment of symptoms related to withdrawal bleeding or the hormone-free interval. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T3 Socioeconomic_factors 1067 1080 contraceptive T2 Socioeconomic_factors 271 303 combined hormonal contraceptives T4 Socioeconomic_factors 1117 1121 CHCs T5 Socioeconomic_factors 1275 1278 CHC T1 Socioeconomic_factors 30 52 hormonal contraception T6 Socioeconomic_factors 305 309 CHCs <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The nutritive value of selected commonly consumed Kuwaiti dishes. The nutritive value of 33 dishes commonly consumed in the state of Kuwait was analyzed. On a fresh weight basis, the moisture content of the dishes ranged from 0.29% to 94.37%, protein content from 0.65% to 24.80%, and fat content from 0.03% to 59.22%. The crude fiber ranged from a low value of 0.04% to a maximum of 2.31%; with the exception of four dishes-Sw-20 (Samsamia), Sw-26 (Harda), V-31 (Hameesat Mushroom), V-32 (Hameesat Fagae)-all dishes had a low fiber level, <1 g/100 g. The ash content ranged from 0.09% to 3.66%. Carbohydrate showed a variation between 3.98% and 81.37%; the highest carbohydrate values were in sweets and rice-based dishes, while the lowest values were in soup and cheese dishes. The energy content of the dishes varied between 19 and 675 kcal/100 g (80-2,823 kJ/100 g). The dishes contributed 5-90%, 1-46% and 1-83% of the total food energy from carbohydrate, protein and fat, respectively. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 50 64 Kuwaiti dishes T2 Nutrition 597 609 Carbohydrate T3 Nutrition 667 679 carbohydrate T4 Nutrition 948 960 carbohydrate T5 Nutrition 695 701 sweets T6 Nutrition 706 723 rice-based dishes T7 Nutrition 757 761 soup T8 Nutrition 766 779 cheese dishes T9 Nutrition 962 969 protein T10 Nutrition 974 977 fat T12 Nutrition 785 799 energy content T13 Nutrition 286 297 fat content T14 Nutrition 244 259 protein content T15 Nutrition 4 19 nutritive value T16 Nutrition 71 86 nutritive value T17 Nutrition 324 335 crude fiber T18 Nutrition 433 441 Samsamia T19 Nutrition 451 456 Harda T20 Nutrition 465 482 Hameesat Mushroom T21 Nutrition 491 505 Hameesat Fagae T22 Nutrition 528 533 fiber T23 Nutrition 557 568 ash content T24 Nutrition 931 942 food energy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Development and initial validation of the White Bear Suppression Inventory (Smoking Version). INTRODUCTION: Thought suppression can produce a paradoxical rebound in unwanted cognition. Although interest in the role of suppression in nicotine dependence is growing quickly, a validated measure specifically assessing suppression of smoking-related thoughts does not exist. The present study describes the development of the White Bear Suppression Inventory-Smoking Version (WBSI-S). METHOD: The WBSI-S, in vivo monitoring of avoidance, and several other measures were completed as a part of a larger study on smoking cessation. Participants (N = 172) completed measures either during (n = 83) or preceding a smoking cessation attempt. RESULTS: Factor analysis revealed a two-factor structure for the WBSI-S, which was consistent across experimental groups. Both the Intrusive Smoking-related Thoughts and Thought Suppression subscales showed strong internal consistency. The Suppression subscale showed good convergent and discriminant validity; the Intrusion subscale demonstrated equivocal discrimination from other constructs. Participants completing the measure during a quit attempt reported higher self-reported suppression of thoughts about smoking than did continuing smokers. CONCLUSIONS: Overall, results support the construct validity of the suppression subscale and emphasize the importance of assessing suppression independently from intrusion. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Drugs 609 626 smoking cessation T3 Drugs 708 725 smoking cessation T4 Drugs 234 253 nicotine dependence T5 Drugs 332 339 smoking T8 Drugs 1248 1255 smoking T7 Drugs 876 883 Smoking <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: First Year Impacts of Casino Gambling in a Community. Gambling has become both a major pastime for Canadians and a multibillion dollar industry providing provincial governments with an increasing proportion of their annual revenues. The continuing trend toward the legalization of gambling has made research on the public health impacts of gambling increasingly important to citizens and decision-makers. This article presents first year results of a multi-year project to measure the impact of the opening of Casino Windsor on gambling behaviour in Windsor, Ontario, Canada. A random telephone survey of gambling behaviour was conducted with 2,682 adult residents of metropolitan Windsor prior to the opening of Casino Windsor, and was repeated with 2,581 residents one year later. There were no statistically significant changes in the rates of problem and pathological gambling among men, women, or the general population one year following the opening of the casino. Although there was some evidence of higher-spending gamblers within the post-casino sample, no statistically significant differences were found between pre- and post-casino per capita gambling expenditures. Implications of these results for the future measurement and treatment of problem and pathological gambling are discussed. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Non_physical_leisure_time_activities 22 37 Casino Gambling T2 Non_physical_leisure_time_activities 55 63 Gambling T3 Non_physical_leisure_time_activities 282 290 gambling T4 Non_physical_leisure_time_activities 606 624 gambling behaviour T5 Non_physical_leisure_time_activities 341 349 gambling T6 Non_physical_leisure_time_activities 529 547 gambling behaviour T10 Non_physical_leisure_time_activities 88 95 pastime T15 Non_physical_leisure_time_activities 1024 1032 gamblers T9 Non_physical_leisure_time_activities 1156 1164 gambling T7 Non_physical_leisure_time_activities 873 881 gambling T8 Non_physical_leisure_time_activities 1278 1286 gambling <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The development of scales to measure the experience of self-participation in sleep. Recent literature suggests that how the self is represented is a cardinal aspect of the dream experience. A review of studies of phasic-tonic distinctions within rapid eye movement (REM) sleep revealed that increasing self-participation marked awakenings from phasic intervals. Five scales specifically designed to measure absorption in dreaming were compared with three scales previously shown to discriminate phasic from tonic awakenings within REM sleep. Eight reports per night (two stage REM, six stage 2) were collected from each of 20 subjects on 4 baseline nights. Awakenings were controlled for time into the REMP and time of night, correcting the methodological flaws of previous studies in this area. Scales developed to measure self-participation were able to discriminate phasic from tonic awakenings better than those already in the literature. Results are discussed in terms of the psychometric properties of the individual scales. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Sleep 77 82 sleep T2 Sleep 247 277 rapid eye movement (REM) sleep T3 Sleep 532 541 REM sleep T4 Sleep 578 581 REM T5 Sleep 587 594 stage 2 <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Responsible Gambling Research and Industry Funding Biases. This brief report examines whether there are differences in aspects of different characteristics, including design/methodologies of responsible gambling (RG), between studies funded by industry as compared to other sources. To investigate this, the authors used those studies included in a recent meta-analysis focusing on the empirical basis of RG initiatives (Ladouceur et al. in Addict Res Theory 25:225-235, 2017). We examined eight associations between funding sources, and different design/methodological characteristics of these studies; type of strategy, inclusion of comparison groups, measurement scales and repeated measures, publication source, number of inclusion criteria met, secondary sources of funding, publication year. The results revealed no statistically significant difference between the funding source, and the index study characteristics. These results do not support claims that funding exerts influence on the design or methodologies of RG studies. However, the absence of statistically significant findings should not be used to assert the absence of a funding effect because there are many reasons for failing to find differences, or interpretation of findings. Unexpectedly, a third of the papers included in this study failed to disclose their funding sources. This finding highlights the need for more open and transparent disclosures. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Non_physical_leisure_time_activities 0 20 Responsible Gambling T2 Non_physical_leisure_time_activities 192 212 responsible gambling T3 Non_physical_leisure_time_activities 406 408 RG T4 Non_physical_leisure_time_activities 1025 1027 RG T5 Non_physical_leisure_time_activities 214 216 RG <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Dental disease preventive programs in a rural Appalachian population. The objective of the dental disease prevention and early intervention program at Youngstown State University is to 1) educate future dental hygienists on the provision of primary care in a non-traditional dental care setting and 2) provide access to preventive and simple restorative care for an underserved rural Appalachian population. Through their education in dental public health courses, students are prepared to provide preventive oral healthcare services to underserved populations in non-traditional clinical settings. Senior dental hygiene students in the dental public health course sequence work in pairs, along with a supervising dentist using a mobile dental unit at the Child and Family Health Services Clinic in Lisbon, Ohio. Ninety-eight hours of preventive oral health services and simple restorative care were provided in 1996. Student evaluations of the overall extramural clinical experience were positive. The program is an example of a collaborative community-based approach to meet the oral health needs of low-income families. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T7 Beauty_and_Cleaning 847 858 oral health T8 Beauty_and_Cleaning 1082 1093 oral health T9 Socioeconomic_factors 1103 1122 low-income families <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Practical issues and the assessment of pathological gamblers in a private practice setting. With increased awareness and knowledge of pathological gambling comes the opportunity to share various perspectives and emerging skills. Descriptions of the assessment and treatment of the pathological gambler, spouse, and family have mushroomed in the past twenty years. However, most treatment summaries have come out of institutional or government funded settings. This article discusses various practical aspects of working with pathological gamblers in an outpatient, fee for service private practice setting. The author also describes refinements made in the assessment process since he first began working with pathological gamblers in 1979. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Non_physical_leisure_time_activities 148 156 gambling <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Is couple therapy indicated for borderline personality disorder? The healing aspects of intimate relationships are being recognized by therapists and supported by empirical findings. Little has been written regarding the indications for couple therapy for individuals with borderline personality disorder. This paper proposes a three-level hierarchy for deciding on the suitability of couple therapy for patients with borderline personality disorder. The hierarchy is based on three separate borderline behavioral clusters--impulsive, identity, and affective--that should be utilized to direct the application of couple therapy to specific patients. The three levels are illustrated by case examples. Delineation of the indications for couple therapy for borderline patients will foster further theoretical and empirical work in the area. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 3 17 couple therapy T2 Mental_health_practices 238 252 couple therapy T3 Mental_health_practices 386 400 couple therapy T4 Mental_health_practices 614 628 couple therapy T5 Mental_health_practices 737 751 couple therapy T6 Socioeconomic_factors 89 111 intimate relationships <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Determination of observer-rated alpha activity during sleep. Patients suffering from chronic fatigue syndrome (CFS) have been described as having alpha intrusion into sleep. In a separate study of the relationship between depression and CFS, we investigated the sleep of CFS patients. We could not detect any observable alpha anomaly in our group of CFS patients. It is possible that there is a subgroup of CFS patients in whom no alpha anomaly is present. However, the sleep electroencephalogram (EEG) montage used in our study was different to that employed by previous researchers. This paper investigates the influence of electrode derivations on the outcome of observable alpha ratings. We compared simultaneous recordings of sleep EEG using three commonly employed montages. Our results indicate that use of the mastoid reference (montage 1) results in the highest observer-related alpha. This may suggest that data regarding alpha intrusion should always be collected using montage 1. However, there is a possibility that the mastoid electrode is not electrically silent and is contaminating the data of the referenced channels. The implications of these findings are discussed in relation to the validity of alpha intrusion measurement of CFS and fibromyalgia. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Sleep 54 59 sleep T2 Sleep 168 173 sleep T3 Sleep 263 268 sleep T4 Sleep 471 476 sleep T5 Sleep 732 737 sleep <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Real-world vehicle emissions: a summary of the Seventeenth Coordinating Research Council On-Road Vehicle Emissions Workshop. The Coordinating Research Council, Inc. (CRC) held its 17th On-Road Vehicle Emissions Workshop in March 2007, where results of the most recent on-road vehicle emissions research were presented. We summarize ongoing work from researchers who are engaged in improving our understanding of the role and contribution of mobile sources to ambient air quality and emission inventories. Participants in the Workshop discussed efforts to improve mobile source emission models, light- and heavy-duty vehicle emissions measurements, on- and off-road emissions measurements, effects of fuels and lubricating oils on emissions, as well as emerging issues and topics for future research. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T6 Environmental_exposures 468 479 air quality T1 Environmental_exposures 11 28 vehicle emissions T11 Environmental_exposures 701 706 fuels T12 Environmental_exposures 711 727 lubricating oils T13 Environmental_exposures 731 740 emissions <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Evaluating national and subnational CO2 mitigation goals in China's thirteenth five-year plan from satellite observations. Good-quality emission data are essential to validate national climate commitments and implement domestic policies. However, conventional bottom-up data collection is costly and subject to potential manipulation by stakeholders when data pass through their hands, which could pose challenges on the efficiency and effectiveness of compliance monitoring especially in developing countries. Satellite observations, specifically OCO-2 XCO2 measurements, are utilized in this study to develop a relatively independent and timely dataset for screening the attainment statuses of national and subnational CO2 mitigation goals in China's 13th Five-Year Plan (FYP, 2016-2020). We establish CO2 emission estimation models at both pixel and provincial levels. As interpolated with the pixel-level model, CO2 emissions of prefecture-level municipalities indicate that approximately three fifth of them had accomplished their individualized FYP mitigation goals by 2019, while our provincial-level estimation suggests that three quarters of provinces had attained theirs. More resources for compliance monitoring could thus be directed to other presumably-unattaining local governments. National aggregate absolute emissions showed 8.0-18.3% reduction in 2019 across three provincial models from the 2015 level, while national CO2 intensity dropped by 28.8-36.8% to imply attaining the 18% reduction goals. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Environmental_exposures 186 193 climate T4 Environmental_exposures 917 930 CO2 emissions T5 Environmental_exposures 805 817 CO2 emission T6 Socioeconomic_factors 490 510 developing countries T7 Environmental_exposures 36 39 CO2 T8 Environmental_exposures 722 725 CO2 T10 Environmental_exposures 1326 1335 emissions <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Aliveness in the work of the group: a subjective guide to creative character change. The subjective response of aliveness in the work of the group may be a valuable signal on the journey toward creative character change in group therapy. In order to promote change in others, the group therapist must engage deeply and use internal responses as guides during interactions with group members as well as in relation to the group as a whole. Subjective awareness of increased aliveness that is linked with a sense of the work of the group may guide both therapist and group participants in the midst of inevitable anxieties and passions aroused during this quest for new vitality and freedom in relationships in the group. Winnicott's concept of potential space as well as group-relations theory about the primary task provide a conceptual foundation for this approach not only on the level of the individual member within the group but also on the level of the group as a whole. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 224 237 group therapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Effectiveness of Prison-Based Exercise Training Programs: A Systematic Review. BACKGROUND: The promotion of physical activity among imprisoned people is a public health strategy that could help to improve the health status of this collective. This systematic review is aimed at reviewing the scientific evidence regarding the effects of exercise training programs performed by inmates. METHODS: A systematic search for randomized controlled trials aimed at identifying the characteristics and effects of prison-based exercise training programs on imprisoned people was carried through MEDLINE, SPORTDiscus, and Scopus. RESULTS: A total of 11 randomized controlled studies were selected, and the methodological quality of these was acceptable according to the Downs and Black scale. The proposed interventions were mainly based on the performance of aerobic or combined exercise training programs. Generally, the participants were healthy men who were imprisoned for at least 2 months and up to 15 years. Ten out of the 11 studies reported significant changes on physical and mental health-related variables, after the intervention took place. CONCLUSION: These findings suggest that prison-based exercise programs constitute a feasible and useful strategy for improving the physical and mental health status of prisoners. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T3 Physical_activity 109 126 physical activity T6 Physical_activity 338 355 exercise training T7 Physical_activity 861 887 combined exercise training T1 Physical_activity 17 47 Prison-Based Exercise Training T2 Physical_activity 505 535 prison-based exercise training T4 Physical_activity 1184 1205 prison-based exercise T5 Physical_activity 850 857;870 887 aerobic exercise training <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Structured Play Therapy Groups for Preschoolers: facilitating the emergence of social competence. Over the years, we have developed a working model of Structured Play Therapy Groups for Preschoolers, an innovative treatment approach designed to address the needs of young children ages 3 to 5 struggling to adjust to the social demands of their preschool classrooms. These short-term therapy groups facilitate development of the young child's social competence and capacity to participate effectively in a classroom environment. Although the literature on therapy groups for children suggests that preschoolers are not yet evolved enough developmentally to engage actively in a group process, our experience indicates otherwise. The model of treatment presented here will therefore challenge that contention with the claim that not only can preschoolers participate in a structured therapy group of peers but they can, by virtue of that very participation, benefit in ways that will prepare them (as they transition from preschool to kindergarten) for the ever-increasing demands of their ever-expanding social milieus. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 0 23 Structured Play Therapy T13 Socioeconomic_factors 1022 1031 preschool T14 Socioeconomic_factors 1035 1047 kindergarten T6 Socioeconomic_factors 346 355 preschool T2 Mental_health_practices 152 175 Structured Play Therapy T4 Mental_health_practices 385 392 therapy T5 Mental_health_practices 557 564 therapy T7 Mental_health_practices 883 890 therapy T9 Socioeconomic_factors 1105 1119 social milieus <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Deception Detection: The Relationship of Levels of Trust and Perspective Taking in Real-Time Online and Offline Communication Environments. Where humans have been found to detect lies or deception only at the rate of chance in offline face-to-face communication (F2F), computer-mediated communication (CMC) online can elicit higher rates of trust and sharing of personal information than F2F. How do levels of trust and empathetic personality traits like perspective taking (PT) relate to deception detection in real-time CMC compared to F2F? A between groups correlational design (N = 40) demonstrated that, through a paired deceptive conversation task with confederates, levels of participant trust could predict accurate detection online but not offline. Second, participant PT abilities could not predict accurate detection in either conversation medium. Finally, this study found that conversation medium also had no effect on deception detection. This study finds support for the effects of the Truth Bias and online disinhibition in deception, and further implications in law enforcement are discussed. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Longitudinal Association Between Physical Activity and Body Fat During Adolescence: A Systematic Review. BACKGROUND: Physical activity (PA) practice has been inversely associated to body fat (BF) and recommended as a way to reduce and prevent obesity. The objective of this study was to conduct a systematic review on the association of PA and BF in adolescence. METHODS: The review includes 18 longitudinal studies found in the PubMed database, comprising papers published from January 1990 to July 2014. Studies assessing BF only through body mass index were excluded. RESULTS: Among the outcomes analyzed, waist circumference, skinfolds, and absolute and relative fat mass measurement were identified. Questionnaires were the more predominant way to evaluate PA. Most studies showed that PA promotes a protective effect against a higher BF gain. CONCLUSION: It was concluded that PA has a protective effect against BF with differences between the genders and according to the BF marker or measurement assessed; higher intensity PA leads to a greater effect against BF gain in both genders; and the maintenance or increase of PA level on BF observed through analysis of change in PA level yielded more consistent findings in the relation between PA and BF. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 33 50 Physical Activity T2 Physical_activity 118 135 Physical activity T3 Physical_activity 338 340 PA T4 Physical_activity 763 765 PA T5 Physical_activity 792 794 PA T6 Physical_activity 884 886 PA T7 Physical_activity 1015 1034 higher intensity PA T8 Physical_activity 1129 1137 PA level T9 Physical_activity 1183 1191 PA level T10 Physical_activity 1249 1251 PA T13 Physical_activity 137 139 PA <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Clothing and thermoregulation during exercise. Exercise increases heat production. During exercise in both warm and cold conditions, the major dilemma is the dissipation of the heat produced from muscular activity. The use of clothing generally represents a layer of insulation and as such imposes a barrier to heat transfer and evaporation from the skin surface. In warm environments, additional clothing increases thermal insulation causing more rapid increases in temperature during exercise and imposes a barrier to sweat evaporation. However, clothing can serve a protective function by reducing radiant heat gain and thermal stress. Recent research suggests that neither the inclusion of modest amounts of clothing nor the clothing fabric alter thermoregulation or thermal comfort during exercise in warm conditions. In the cold, most reports do not support an effect of clothing fabric on thermoregulation; however, there are reports demonstrating an effect. Clothing construction does alter thermoregulation during and following exercise in the cold, where fishnet construction offers greater heat dissipation. Future research should include conditions that more closely mimic outdoor conditions, where high work rates, large airflow and high relative humidity can significantly impact thermoregulation. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T9 Physical_activity 37 45 exercise T10 Physical_activity 48 56 Exercise T11 Physical_activity 1038 1046 exercise T12 Physical_activity 795 803 exercise T13 Physical_activity 487 495 exercise T14 Physical_activity 91 99 exercise T24 Environmental_exposures 1252 1269 relative humidity T23 Physical_activity 1217 1227 work rates T26 Environmental_exposures 1229 1242 large airflow T1 Environmental_exposures 368 385 warm environments T17 Physical_activity 206 214 activity <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Mental health correlates of intimate partner violence in marital relationships in a nationally representative sample of males and females. It is important to understand the epidemiology of intimate partner violence (IPV) experienced by both males and females. Data were drawn from the U.S. National Comorbidity Survey Replication. The relationships between physical IPV and child abuse, mental disorders, and suicidal ideation and attempts among males and females were examined. The results indicate that child sexual abuse was associated with IPV among males, whereas child physical and sexual abuse was associated with IPV among females. IPV was associated with poor mental health outcomes for males and females, although sex differences are noted. The sex differences indicate that females experience a wider range of poor mental health outcomes compared to males. Knowledge about correlates of IPV can be useful in identifying individuals exposed to violence. Further research is required to identify effective methods to reduce exposure to IPV and to adequately address the specific needs of male and female victims of IPV. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 28 53 intimate partner violence T2 Socioeconomic_factors 190 215 intimate partner violence T3 Socioeconomic_factors 358 370 physical IPV T4 Socioeconomic_factors 545 548 IPV T5 Socioeconomic_factors 622 625 IPV T6 Socioeconomic_factors 1125 1128 IPV T7 Socioeconomic_factors 1046 1049 IPV T8 Socioeconomic_factors 641 644 IPV T9 Socioeconomic_factors 899 902 IPV T10 Socioeconomic_factors 506 524 child sexual abuse T11 Socioeconomic_factors 57 78 marital relationships T12 Socioeconomic_factors 375 386 child abuse T13 Socioeconomic_factors 570 584;596 601 child physical abuse T14 Socioeconomic_factors 944 963 exposed to violence T16 Socioeconomic_factors 217 220 IPV T17 Socioeconomic_factors 570 575;589 601 child sexual abuse <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The effect of antioxidant vitamins E and C on cognitive performance of the elderly with mild cognitive impairment in Isfahan, Iran: a double-blind, randomized, placebo-controlled trial. PURPOSE: This study was carried out to investigate the effect of vitamins E and C on cognitive performance among the elderly in Iran. METHODS: About 256 elderly with mild cognitive impairment, aged 60-75 years, received 300 mg of vitamin E plus 400 mg of vitamin C or placebo daily just for 1 year. BACKGROUND: Demographic characteristics, anthropometric variables food consumption, cognitive function by Mini-Mental State Examination (MMSE), and some of the oxidative stress biomarkers were examined. RESULTS: Antioxidant supplementation reduced malondialdehyde level (P < 0.001) and raised total antioxidant capacity (P < 0.001) and glutathione (P < 0.01). The serum 8-hydroxydeoxyguanosine remained unchanged (P < 0.4). After adjusting for the covariates effects, MMSE scores following 6- (25.88 +/- 0.17) and 12-month antioxidant supplementation (26.8 +/- 0.17) did not differ from control group (25.86 +/- 0.18 and 26.59 +/- 0.18, respectively). CONCLUSION: Despite significant improvement in most of the oxidative stress biomarkers, antioxidants' supplementation was not observed to enhance cognitive performance. A large number of kinetic and/or dynamic factors could be suspected. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 14 36 antioxidant vitamins E T5 Nutrition 252 262 vitamins E T6 Nutrition 417 426 vitamin E T7 Nutrition 442 451 vitamin C T8 Nutrition 455 462 placebo T9 Nutrition 160 167 placebo T10 Nutrition 1226 1255 antioxidants' supplementation T11 Nutrition 1009 1036 antioxidant supplementation T12 Nutrition 698 725 Antioxidant supplementation T14 Nutrition 552 568 food consumption T15 Nutrition 14 34;41 42 antioxidant vitamins C T16 Nutrition 252 260;267 268 vitamins C <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Bullying Perpetration and Victimization: A Test of Traditional and Cyber-Behaviors as Latent Constructs. The current investigation hypothesized and tested latent bully/victim traits for physical, verbal, or relational bullying/victimization, both cyber and traditional behaviors. Data were collected from 1,356 German students who attended Grades 5 to 10: 48.4% males, 49.3% females from eight different schools in Northern Germany. Based on two samples for cross-validation (Ntraining set = 525, Nvalidation set = 525), study findings provided strong evidence of adequate model fit, both for traditional and cyber behaviors. Consistent with the current state of knowledge, bullying and victimization latent traits highly associated, more so for cyber behaviors than traditional ones. Thus, both the theoretical plausibility as well as statistical evidence support the application of latent modeling to these behaviors. Further research is needed to replicate the applied measurement models proposed in this work and to reveal moderators or measurement invariance across diverse populations. Nevertheless, the current evidence substantiates the importance of the application of a latent modeling approach to overcome known psychometric challenges of reliability and validity in bullying research. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 0 8 Bullying T2 Socioeconomic_factors 219 227 bullying T3 Socioeconomic_factors 675 683 bullying T4 Socioeconomic_factors 1279 1287 bullying T5 Socioeconomic_factors 688 701 victimization T6 Socioeconomic_factors 26 39 Victimization T7 Socioeconomic_factors 228 241 victimization <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Agreement between virtual and in-the-field environmental audits of assisted living sites. The authors investigated the use of Google Earth's Street View option to audit the presence of built environment features that support older adults' walking. Two raters conducted virtual (Street View) and in-the-field audits of 48 street segments surrounding urban and suburban assisted living sites in metropolitan Vancouver, BC, Canada. The authors determined agreement using absolute agreement. Their findings indicate that Street View may identify the presence of features that promote older adults' walking, including sidewalks, benches, public washrooms, and destinations. However, Street View may not be as reliable as in-the-field audits to identify details associated with certain items, such as counts of trees or street lights; presence, features, and height of curb cuts; and sidewalk continuity, condition, and slope. Thus, the appropriateness of virtual audits to identify microscale built environment features associated with older adults' walking largely depends on the purpose of the audits-specifically, whether the measurer seeks to capture highly detailed features of the built environment. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T3 Physical_activity 595 602 walking T4 Physical_activity 1046 1053 walking T5 Physical_activity 240 247 walking T6 Socioeconomic_factors 67 88 assisted living sites T7 Socioeconomic_factors 360 390 suburban assisted living sites T8 Socioeconomic_factors 350 355;369 390 urban assisted living sites <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Continuing education. A large, representative sample of licensed dental hygienists practicing in the United States was surveyed by ADHA regarding dental hygiene demographics, office characteristics, supervision and practice behavior, infection control practices, utilization of medical and dental histories, and continuing education. This paper presents the continuing education portion of this survey and specifically explores relationships among descriptive variables such as dental hygienists' ages, types of practice, membership in ADHA, continuing education courses attended, and interest versus need in continuing education course attendance. Results indicate that continuing education participation is most likely for dental hygienists under 25 years of age and who are working over 30 hours per week in a speciality office which employs seven or more staff members. Comparisons among dentist, dental hygienist, and dental assistant participation were made for selected continuing education topics. According to the respondents, more dental hygienists than dentists or dental assistants have participated in continuing education courses on blood pressure measurement, aseptic technique, AIDs, and cardiopulmonary resuscitation. Dental hygienists perceived a need for attendance by all dental personnel in selected continuing education programs. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T7 Beauty_and_Cleaning 147 161 dental hygiene <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Home smoking restrictions and adolescent smoking. The prevention of adolescent smoking has focused on peer influences to the relative neglect of parental influences. Parents socialize their children about many behaviors including smoking, and parental rules about their child's smoking have been related to lower levels of adolescent smoking. Moreover, among adults, indoor smoking restrictions have been associated with decreased smoking. Accordingly, the current study tested the relation of adolescent smoking to home smoking policy (rules regulating where adults are allowed to smoke in the home). Results showed that restrictive home smoking policies were associated with lower likelihood of trying smoking for both middle and high school students. However, for high school students this relation was restricted to homes with non-smoking parents. Home smoking policies were not associated with current regular smoking for either middle or high school students. Home smoking policies may be useful in preventing adolescent smoking experimentation, although longitudinal and experimental research is necessary to confirm this hypothesis. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Drugs 0 12 Home smoking T2 Drugs 30 48 adolescent smoking T3 Drugs 69 87 adolescent smoking T4 Drugs 231 238 smoking T5 Drugs 279 286 smoking T6 Drugs 324 342 adolescent smoking T7 Drugs 368 382 indoor smoking T8 Drugs 432 439 smoking T9 Drugs 495 513 adolescent smoking T10 Drugs 517 529 home smoking T11 Drugs 583 600 smoke in the home T12 Drugs 635 647 home smoking T13 Drugs 705 712 smoking T14 Socioeconomic_factors 733 753 high school students T15 Socioeconomic_factors 768 788 high school students T16 Socioeconomic_factors 832 851 non-smoking parents T17 Drugs 853 865 Home smoking T18 Drugs 908 923 regular smoking T19 Socioeconomic_factors 945 965 high school students T20 Drugs 967 979 Home smoking T21 Drugs 1017 1035 adolescent smoking T22 Socioeconomic_factors 722 728;738 753 middle school students T23 Socioeconomic_factors 935 941;950 965 middle school students <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Coding "We-ness" in couple's relationship stories: A method for assessing mutuality in couple therapy. OBJECTIVE: "We-ness," a couple's mutual investment in their relationship and in each other, has been found to be a potent dimension of couple resilience. This study examined the development of a method to capture We-ness in psychotherapy through the coding of relationship narratives co-constructed by couples ("We-Stories"). It used a coding system to identify the core thematic elements that make up these narratives. METHOD: Couples that self-identified as "happy" (N = 53) generated We-Stories and completed measures of relationship satisfaction and mutuality. These stories were then coded using the We-Stories coding manual. RESULTS AND CONCLUSIONS: Findings indicated that security, an element that involves aspects of safety, support, and commitment, was most common, appearing in 58.5% of all narratives. This element was followed by the elements of pleasure (49.1%) and shared meaning/vision (37.7%). The number of "We-ness" elements was also correlated with and predictive of discrepancy scores on measures of relationship mutuality, indicating the validity of the We-Stories coding manual. Limitations and future directions are discussed. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Mental_health_practices 87 101 couple therapy T5 Mental_health_practices 328 341 psychotherapy T7 Socioeconomic_factors 628 653 relationship satisfaction <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Attentional bias in addictive behaviors: a review of its development, causes, and consequences. A wealth of research from the past two decades shows that addictive behaviors are characterized by attentional biases for substance-related stimuli. We review the relevant evidence and present an integration of existing theoretical models to explain the development, causes, and consequences of addiction-related attentional biases. We suggest that through classical conditioning, substance-related stimuli elicit the expectancy of substance availability, and this expectancy causes both attentional bias for substance-related stimuli and subjective craving. Furthermore, attentional bias and craving have a mutual excitatory relationship such that increases in one lead to increases in the other, a process that is likely to result in substance self-administration. Cognitive avoidance strategies, impulsivity, and impaired inhibitory control appear to influence the strength of attentional biases and subjective craving. However, some measures of attentional bias, particularly the addiction Stroop, might reflect multiple underlying processes, so results need to be interpreted cautiously. We make several predictions that require testing in future research, and we discuss implications for the treatment of addictive behaviors. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Drugs 833 862 substance self-administration T2 Drugs 20 39 addictive behaviors T4 Drugs 155 174 addictive behaviors T5 Drugs 1308 1327 addictive behaviors T7 Drugs 392 401 addiction T9 Drugs 529 538 substance <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Contribution of (222)Rn-bearing water to the occupational exposure in thermal baths. The present study investigates the short- and long-term effects of radon ((222)Rn) released from water on the progeny exposure in a thermal spa. For the purposes of this work, the Polichnitos spa was used as a case study. The bathroom was supplied with water containing 110-210 kBq m(-3) of (222)Rn. The (222)Rn concentration in air and the short-lived (222)Rn progenies in attached and unattached form were monitored into the bathroom and the surrounding premises. The equilibrium factor (F-factor) and the unattached fraction were estimated. The results of this study show that water flow during bath filling is by far the dominant mechanism by which (222)Rn is released in the air of the bathroom. The progeny exposure was correlated linearly with the (222)Rn concentration in the entering water. The annual effective dose received by a worker was found to be below the lower limit value of 3 mSv recommended by ICRP 65. The dose limit was exceeded only for water containing more than 300 kBq m(-3). <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 45 66 occupational exposure T2 Environmental_exposures 153 158 radon T3 Environmental_exposures 196 212 progeny exposure T4 Environmental_exposures 390 418 (222)Rn concentration in air T5 Environmental_exposures 791 807 progeny exposure T13 Environmental_exposures 160 167 (222)Rn T14 Environmental_exposures 439 446 (222)Rn T15 Environmental_exposures 377 384 (222)Rn T16 Environmental_exposures 16 37 (222)Rn-bearing water T17 Environmental_exposures 739 746 (222)Rn T18 Environmental_exposures 841 848 (222)Rn <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Uranium levels in the diet of Sao Paulo City residents. Natural levels of uranium in the diet of Sao Paulo City residents were studied, and radionuclide concentrations were measured by the fission track method on samples of typical adult food items. This information was used to evaluate the daily intake of uranium in individuals living in Sao Paulo City which is, according to our findings, around 0.97 microg U/day. Using the ICRP Uranium-model, we estimated the uranium accumulation and committed doses in some tissues and organs, as function of time. We compared the output of the ICRP uranium biokinetic model, tailored for the conditions prevailing in Sao Paulo, with experimental data from other localities. Such comparison was possible by means of a simple method we developed, which allows normalization among experimental results from different regions where distinct values of chronic daily intake are observed. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T7 Nutrition 225 249 typical adult food items T4 Environmental_exposures 75 82 uranium T1 Environmental_exposures 0 7 Uranium T2 Environmental_exposures 293 316 daily intake of uranium T5 Nutrition 22 26 diet T8 Nutrition 90 94 diet T11 Environmental_exposures 467 474 uranium <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The ethical group psychotherapist: a coda. This concluding article of a two-part special section on the ethical group psychotherapist are highlighted: (1) the importance of the cultivation of the skill and knowledge base of the group psychotherapist in terms of pertinent legal statutes and ethical guidelines; (2) the criticalness of certain personality features related to the concept of virtue; (3) a therapist's self-awareness contributes to the capacities to think and respond ethically; (4) ethical decision making is most likely to occur when the group psychotherapist attends comprehensively to all dimensions that define the setting in which a dilemma emerges; and (5) that in the service of positive ethics, attention to the ethical dimensions of group psychotherapy practice should be continuously present. In agreement with other contributions to this section, I conclude that the commitment to the ethical practice of group psychotherapy must be made not only by the individual practitioner but also by educational and training programs and professional organizations. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T5 Mental_health_practices 758 777 group psychotherapy T6 Mental_health_practices 932 951 group psychotherapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Time-limited group psychotherapy. The presence of a time limit shapes the entire therapeutic process and culminates in the management of termination. This paper outlines a protocol for dealing with termination in time-limited psychotherapy groups: (1) Select suitable members with similar capacity to use a time-limited format; (2) use a closed group format; (3) clarify the time boundaries at the beginning; (4) reinforce the time frame during the last half of the group; (5) provide focused interventions; (6) forestall premature termination; (7) reinforce termination themes of deprivation, resentment and anger, rejection, grief and loss, responsibility for self; (8) structure the final session; and (9) plan a 4-month follow-up visit to encourage ongoing application. These guidelines provide the therapist with a general structure to ensure that major termination themes are systematically addressed. They deal with powerful concepts of maturation and self-responsibility and incorporate the principal strategies that distinguish time-limited from time-unlimited group psychotherapy. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 13 32 group psychotherapy T2 Mental_health_practices 1071 1090 group psychotherapy T3 Mental_health_practices 227 240 psychotherapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Orbital fractures in sport: a review. Orbital fractures represent a small proportion of sports-related injuries in the US and abroad. However, the significant functional and cosmetic sequelae of such fractures warrant a comprehensive evaluation of any athlete sustaining facial trauma. Initial evaluation is directed at excluding neurological impairment or the presence of vision-threatening injury. Subsequent examination should ascertain the presence of signs and symptoms consistent with orbital fracture. These include gross bony deformity, limitation of gaze, diplopia and malposition of the globe. The presence of any of the aforementioned symptoms should prompt further investigation using computed tomography to corroborate or refute clinical suspicion. Orbital fracture mandates referral to an ophthalmologist or oculoplastic surgeon; initial management is dictated by the severity of functional symptoms, and may necessitate early surgical intervention. Those patients who are managed conservatively should return for frequent follow-up, as progressive diplopia, enophthalmos, or gaze limitation are indications for late operative repair. The majority of patients who sustain orbital fractures are able to return to sport, however, persistent diplopia is not uncommon, and may persist despite optimal treatment. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 21 26 sport T2 Physical_activity 89 95 sports T7 Physical_activity 1227 1232 sport <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Stability of carotenoids and tocopherols in ready-to-eat baby-leaf lettuce and salad rocket during low-temperature storage. Minimally processed ready-to-eat baby-leaf vegetables (BLVs) are the most convenient source to include the health beneficial bioactive in the daily diet. In the present study, the visual quality and storage stability of carotenoids, tocopherols were investigated in lettuce (green and red romaine) and salad rocket BLVs. The commercially packed samples of BLVs were stored at 0 degreesC and 4 degreesC in dark conditions and analyzed after 0, 2, 4, 8 and 12 days of storage. All the studied samples were found in better visual quality up to eight days of storage at both the temperatures. In most cases, the quality was correlated with the chlorophyll contents. The highest significant (p < 0.05) positive changes in total carotenoids and tocopherols were observed in samples stored at 4 degreesC. Also, carotenoids and tocopherols are maximum stable in green and red romaine lettuce, respectively. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 13 24 carotenoids T2 Nutrition 345 356 carotenoids T3 Nutrition 848 859 carotenoids T4 Nutrition 929 940 carotenoids T5 Nutrition 29 40 tocopherols T6 Nutrition 358 369 tocopherols T7 Nutrition 864 875 tocopherols T8 Nutrition 945 956 tocopherols T9 Nutrition 57 74 baby-leaf lettuce T10 Nutrition 158 178 baby-leaf vegetables T11 Nutrition 267 277 daily diet T12 Nutrition 391 398 lettuce T13 Nutrition 427 445 salad rocket BLVs. T14 Nutrition 481 485 BLVs T15 Nutrition 989 1008 red romaine lettuce T16 Nutrition 79 91 salad rocket T17 Nutrition 180 184 BLVs T18 Nutrition 391 398;400 405 lettuce green T19 Nutrition 391 398;410 421 lettuce red romaine T20 Nutrition 1001 1008;979 984 lettuce green <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The antioxidant alpha-lipoic acid attenuates intermittent hypoxia-related renal injury in a mouse model of sleep apnea. STUDY OBJECTIVES: Oxidative stress and inflammation are proposed to mediate kidney diseases that occur in patients with obstructive sleep apnea (OSA). We examined the hypothesis that alpha-lipoic acid (LA) prevents renal injury in a mouse model of sleep apnea. METHODS: Twenty male mice received either intermittent hypoxia (IH) or intermittent air (IA) with or without a LA-enriched diet for 60 days. Plasma and urine samples were collected at the end of study, and kidneys were harvested for protein analysis. RESULTS: Animals in the IH + LA group had lower levels (p < 0.05) of plasma oxidative stress (8-isoprostane) and inflammation (TNF-alpha). Renal oxidative stress was also reduced in mice in the IH + LA (p < 0.05) compared with IH-exposed mice that were fed regular diet. Renal cellular apoptosis and tubular injury were reduced in the IH + LA group. Treatment with LA attenuated IH-induced glomerular hypertrophy and increased albuminuria. CONCLUSIONS: Treatment with LA prevents IH-induced renal injury in mice. LA may be a potential therapy for reducing renal dysfunction in patients with OSA. LA can also increase oxidative stress in healthy animals. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 4 44 antioxidant alpha-lipoic acid attenuates T5 Nutrition 890 902 regular diet T6 Nutrition 493 509 LA-enriched diet T7 Nutrition 304 321 alpha-lipoic acid T9 Nutrition 832 834 LA T10 Nutrition 1101 1103 LA T11 Nutrition 1146 1148 LA T12 Nutrition 1229 1231 LA T8 Nutrition 998 1000 LA T14 Nutrition 662 664 LA T15 Nutrition 973 975 LA T16 Nutrition 323 325 LA <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Organoarsenicals in poultry litter: detection, fate, and toxicity. Arsenic contamination in groundwater has endangered the health and safety of millions of people around the world. One less studied mechanism for arsenic introduction into the environment is the use of organoarsenicals in animal feed. Four organoarsenicals are commonly employed as feed additives: arsanilic acid, carbarsone, nitarsone, and roxarsone. Organoarsenicals are composed of a phenylarsonic acid molecule with substituted functional groups. This review documents the use of organoarsenicals in the poultry industry, reports analytical methods available for quantifying organic arsenic, discusses the fate and transport of organoarsenicals in environmental systems, and identifies toxicological concerns associated with these chemicals. In reviewing the literature on organoarsenicals, several research needs were highlighted: advanced analytical instrumentation that allows for identification and quantification of organoarsenical degradation products; a greater research emphasis on arsanilic acid, carbarsone, and nitarsone; identification of degradation pathways, products, and kinetics; and testing/development of agricultural wastewater and solid treatment technologies for organoarsenical-laden waste. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 68 89 Arsenic contamination T2 Environmental_exposures 93 104 groundwater T3 Environmental_exposures 213 220 arsenic T5 Environmental_exposures 307 323 organoarsenicals T6 Environmental_exposures 419 435 Organoarsenicals T7 Environmental_exposures 551 567 organoarsenicals T8 Environmental_exposures 844 860 organoarsenicals T9 Environmental_exposures 699 715 organoarsenicals T12 Environmental_exposures 646 661 organic arsenic T13 Environmental_exposures 1061 1075 arsanilic acid T16 Environmental_exposures 1077 1087 carbarsone T18 Environmental_exposures 1093 1102 nitarsone T21 Environmental_exposures 1256 1283 organoarsenical-laden waste T4 Environmental_exposures 269 285 organoarsenicals T11 Environmental_exposures 0 16 Organoarsenicals T14 Environmental_exposures 1195 1218 agricultural wastewater T22 Environmental_exposures 365 379 arsanilic acid T23 Environmental_exposures 381 391 carbarsone T24 Environmental_exposures 393 402 nitarsone T25 Environmental_exposures 408 417 roxarsone T26 Environmental_exposures 454 472 phenylarsonic acid T28 Environmental_exposures 992 1007 organoarsenical T29 Environmental_exposures 1223 1238 solid treatment T10 Environmental_exposures 349 363 feed additives <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Treatment with methotrexate of a cornual pregnancy following endometrial resection. OBJECTIVES: Endometrial resection is a procedure often performed for treatment of menorrhagia. Despite the fact that amenorrhoea frequently ensues, some normal endometrium can remain present and become the implantation site of a pregnancy. Such an event is uncommon (0.7%); however, the likelihood of an ectopic pregnancy is increased. This case report calls the reader's attention to the risk of intra- and extrauterine pregnancies and the necessity for contraception after endometrial resection, even in cases where amenorrhoea supervenes. CASE: A 46-year-old woman with prior endometrial resection and subsequent amenorrhoea, was diagnosed with a cornual pregnancy. She was successfully treated with systemic methotrexate, which was given in an outpatient clinic. CONCLUSION: Clinicians should be aware of the increased likelihood of an ectopic pregnancy after endometrial resection. All women submitting to this procedure should be counselled about the need for contraception, even in cases where amenorrhoea develops. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 1051 1064 contraception T2 Socioeconomic_factors 540 553 contraception <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: "I'm not the only one!" group therapy with older children and adolescents who have learning disabilities. Children and adolescents with learning disabilities (LD) are vulnerable to psychosocial difficulties. Both the detrimental impact of LD on social adjustment and the significance of peer relations for developmental needs make group therapy a natural intervention. Despite modifications that are required due to their cognitive deficits, many students with LD fit the criteria and can benefit from group psychotherapy. In this paper, a model of group therapy for older children and adolescents with LD is presented. The approach is informed by a combination of self psychology, mutual aid, and interpersonal group therapy. The leaders use special techniques to accommodate the LD and to foster the group process. Examples are presented to illustrate the approach. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 24 37 group therapy T2 Mental_health_practices 550 563 group therapy T4 Mental_health_practices 332 345 group therapy T5 Mental_health_practices 503 522 group psychotherapy T6 Socioeconomic_factors 246 263 social adjustment T7 Socioeconomic_factors 288 302 peer relations T3 Mental_health_practices 699 726 interpersonal group therapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Food, water and ethanol consumption by rats under a fixed-interval schedule of food presentation. Rats could lever press 24 hours a day for 97 mg food pellets under a fixed-interval (FI) 90 second schedule. During the first 4 days, an ethanol solution was the only available fluid, after which both water and ethanol solutions were available. At ethanol concentrations (w/v) of 5%, 7.5% and l0%, total caloric intake and total fluid intake remained constant, while ethanol consumption was inversely proportional to the concentration of the solution. When the FI 90s schedule was changed to FI 45 s, or to FI 180 s, there were only small changes in total caloric intake, total fluid intake and in percentages of total fluid consumption and total caloric intake as ethanol. The data suggest that the intake of ethanol under this fixed-interval schedule depends more on the ethanol concentration than on the calories obtained from ethanol drinking. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 0 4 Food T2 Nutrition 6 11 water T4 Nutrition 79 83 food T5 Nutrition 147 151 food T7 Nutrition 300 305 water T10 Nutrition 403 417 caloric intake T13 Nutrition 655 669 caloric intake T20 Nutrition 906 914 calories T11 Nutrition 746 760 caloric intake <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Genetic factors in human sleep disorders with special reference to Norrie disease, Prader-Willi syndrome and Moebius syndrome. Sleep-wake problems are common in specific inborn errors of metabolism and structure of the central nervous system. Psychological factors, behavioural difficulties, metabolic disturbances, and widespread rather than focal damage to the nervous system are present in many of these diseases and all influence the sleep-wake cycle. However, a number of conditions cause relatively focal damage to the neuroanatomical substrate of sleeping and waking. These include fatal familial insomnia, with involvement of the prion protein gene on chromosome 20, Norrie disease, the Prader-Willi syndrome and the Moebius syndrome. The last three important conditions, although rare, are considered in detail in this review. They result in sensory deprivation, hypothalamic and mid-brain damage, and involve the X-chromosome, chromosome 15, and chromosome 13, respectively. These conditions cause a wide variety of sleep disturbance, including parasomnias, daytime sleepiness, and a condition like cataplexy. The place of the relevant gene products in normal sleep regulation needs further exploration. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Sleep 439 455 sleep-wake cycle T5 Sleep 555 563 sleeping T3 Sleep 1164 1187 normal sleep regulation T6 Sleep 1027 1044 sleep disturbance T8 Sleep 568 574 waking T9 Sleep 1069 1087 daytime sleepiness <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Oral contraceptives and venous thromboembolism: an epidemiological review. New epidemiological studies have demonstrated a higher risk of venous thromboembolism among users of oral contraceptives (OCs) with third-generation progestogens desogestrel or gestodene compared with users of OCs with second-generation progestogens levonorgestrel or norgestrel. As the absolute risk of venous thromboembolism among users of OCs in general is lower in the new studies than in previously published studies, and as several important potential confounders are not controlled for in the new studies, the real difference in risk of venous thromboembolism between users of third- compared with second-generation products may be smaller than indicated in these new studies. At the same time, the risk of myocardial infarction may be smaller among users of third- compared with users of second-generation products. Until further epidemiological data emerge, it is therefore difficult to make general recommendations other than to use OCs with a low estrogen dose, and to make individual recommendations based on age, individual wish, and individual risk factors. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 0 19 Oral contraceptives T2 Socioeconomic_factors 177 196 oral contraceptives T3 Socioeconomic_factors 286 289 OCs T4 Socioeconomic_factors 418 421 OCs T5 Socioeconomic_factors 1019 1022 OCs T6 Socioeconomic_factors 198 201 OCs T7 Socioeconomic_factors 225 237 progestogens T8 Socioeconomic_factors 238 249 desogestrel T9 Socioeconomic_factors 253 262 gestodene T10 Socioeconomic_factors 313 325 progestogens T11 Socioeconomic_factors 326 340 levonorgestrel T12 Socioeconomic_factors 344 354 norgestrel <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Sexually harassing behavior against adolescent girls in rural Bangladesh: implications for achieving millennium development goals. This study examines the extent and type of sexually harassing behavior or intimidations unmarried adolescent girls experienced on their way to school, college or social visits and type of perpetrators in victims' view in rural Bangladesh using data of the 2004 National Nutrition Programme baseline survey. The survey collected self-reported data on sexual harassments of 5,106 girls aged 13-19 years selected randomly. Results reveal that gendered harassments were experienced by 35% of the girls, unwanted sexual attentions by 34%, and sexual intimidations by 14%, yielding prevalence of sexual harassments of any type 43%. Higher girls' education and household economic status heightened their risks of being harassed. Perpetrators were male young spoilt bullies (64%), neighborhood youths (30%), students (22%) and hoodlums (6%). High prevalence of sexual harassments mirrors vulnerability of adolescent girls in the community and deserves to be tackled to achieve millennium development goals (MDGs) in gender equality in health and social development. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 0 27 Sexually harassing behavior T3 Socioeconomic_factors 175 202 sexually harassing behavior T4 Socioeconomic_factors 220 229 unmarried T6 Socioeconomic_factors 482 500 sexual harassments T7 Socioeconomic_factors 572 592 gendered harassments T8 Socioeconomic_factors 631 657 unwanted sexual attentions T9 Socioeconomic_factors 670 690 sexual intimidations T10 Socioeconomic_factors 722 740 sexual harassments T11 Socioeconomic_factors 786 811 household economic status T12 Socioeconomic_factors 985 1003 sexual harassments T13 Socioeconomic_factors 758 781 Higher girls' education <|eot_id|>