harmful, and 49% concluded that evidence did not support either benefit or harm. 96% recommended further research. in 2017, a study assessed the role of systematic reviews produced by cochrane collaboration to inform us private payers' policymaking; it showed that although the medical policy documents of major us private payers were informed by cochrane systematic reviews, there was still scope to encourage the further use. assessing the quality of evidence main article: levels of evidence evidence-based medicine categorizes different types of clinical evidence and rates or grades them according to the strength of their freedom from the various biases that beset medical research. for example, the strongest evidence for therapeutic interventions is provided by systematic review of randomized, well-blinded, placebo-controlled trials with allocation concealment and complete follow-up involving a homogeneous patient population and medical condition. in contrast, patient testimonials, case reports, and even expert opinion have little value as proof because of the placebo effect, the biases inherent in observation and reporting of cases, and difficulties in ascertaining who is an expert (however, some critics have argued that expert opinion "does not belong in the rankings of the quality of empirical evidence because it does not represent a form of empirical evidence" and continue that "expert opinion would seem to be a separate, complex type of knowledge that would not fit into hierarchies otherwise limited to empirical evidence alone."). several organizations have developed grading systems for assessing the quality of evidence. for example, in 1989 the u.s. preventive services task force (uspstf) put forth the following system: level i: evidence obtained from at least one properly designed randomized controlled trial. level ii-1: evidence obtained from well-designed controlled trials without randomization. level ii-2: evidence obtained from well-designed cohort studies or case-control studies, preferably from more than one center or research group. level ii-3: evidence obtained from multiple time series designs with or without the intervention. dramatic results in uncontrolled trials might also be regarded as this type of evidence. level iii: opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees. another example are the oxford cebm levels of evidence published by the centre for evidence-based medicine. first released in september 2000, the levels of evidence provide a way to rank evidence for claims about prognosis, diagnosis, treatment benefits, treatment harms, and screening, which most grading schemes do not address. the original cebm levels were evidence-based on call to make the process of finding evidence feasible and its results explicit. in 2011, an international team redesigned the oxford cebm levels to make them more understandable and to take into account recent developments in evidence ranking schemes. the oxford cebm levels of evidence have been used by patients and clinicians, as well as by experts to develop clinical guidelines, such as recommendations for the optimal use of phototherapy and topical therapy in psoriasis and guidelines for the use of the bclc staging system for diagnosing and monitoring hepatocellular carcinoma in canada. in 2000, a system was developed by the grading of recommendations assessment, development and evaluation (grade) working group. the grade system takes into account more dimensions than just the quality of medical research. it requires users who are performing an assessment of the quality of evidence, usually as part of a systematic review, to consider the impact of different factors on their confidence in the results. authors of grade tables assign one of four levels to evaluate the quality of evidence, on the basis of their confidence that the observed effect (a numeric value) is close to the true effect. the confidence value is based on judgments assigned in five different domains in a structured manner. the grade working group defines 'quality of evidence' and 'strength of recommendations' based on the quality as two different concepts that are commonly confused with each other. systematic reviews may include randomized controlled trials that have low risk of bias, or observational studies that have high risk of bias. in the case of randomized controlled trials, the quality of evidence is high but can be downgraded in five different domains. risk of bias: a judgment made on the basis of the chance that bias in included studies has influenced the estimate of effect. imprecision: a judgment made on the basis of the chance that the observed estimate of effect could change completely. indirectness: a judgment made on the basis of the differences in characteristics of how the study was conducted and how the results are actually going to be applied. inconsistency: a judgment made on the basis of the variability of results across the included studies. publication bias: a judgment made on the basis of the question whether all the research evidence has been taken to account. in the case of observational studies per grade, the quality of evidence starts off lower and may be upgraded in three domains in addition to being subject to downgrading. large effect: methodologically strong studies show that the observed effect is so large that the probability of it changing completely is less likely. plausible confounding would change the effect: despite the presence of a possible confounding factor that is expected to reduce the observed effect, the effect estimate still shows significant effect. dose response gradient: the intervention used becomes more effective with increasing dose. this suggests that a further increase will likely bring about more effect. meaning of the levels of quality of evidence as per grade: high quality evidence: the authors are very confident that the presented estimate lies very close to the true value. in other words, the probability is very low that further research will completely change the presented conclusions. moderate quality evidence: the authors are confident that the presented estimate lies close to the true value, but it is also possible that it may be substantially different. in other words, further research may completely change the conclusions. low quality evidence: the authors are not confident in the effect estimate, and the true value may be substantially different. in other words, further research is likely to change the presented conclusions completely. very low quality evidence: the authors do not have any confidence in the estimate and it is likely that the true value is substantially different from it. in other words, new research will probably change the presented conclusions completely. categories of recommendations in guidelines and other publications, recommendation for a clinical service is classified by the balance of risk versus benefit and the level of evidence on which this information is based. the u.s. preventive services task force uses the following system: level a: good scientific evidence suggests that the benefits of the clinical service substantially outweigh the potential risks. clinicians should discuss the service with eligible patients. level b: at least fair scientific evidence suggests that the benefits of the clinical service outweighs the potential risks. clinicians should discuss the service with eligible patients. level c: at least fair scientific evidence suggests that the clinical service provides benefits, but the balance between benefits and risks is too close for general recommendations. clinicians need not offer it unless individual considerations apply. level d: at least fair scientific evidence suggests that the risks of the clinical service outweigh potential benefits. clinicians should not routinely offer the service to asymptomatic patients. level i: scientific evidence is lacking, of poor quality, or conflicting, such that the risk versus benefit balance cannot be assessed. clinicians should help patients understand the uncertainty surrounding the clinical service. grade guideline panelists may make strong or weak recommendations on the basis of further criteria. some of the important criteria are the balance between desirable and undesirable effects (not considering cost), the quality of the evidence, values and preferences and costs (resource utilization). despite the differences between systems, the purposes are the same: to guide users of clinical research information on which studies are likely to be most valid. however, the individual studies still require careful critical appraisal. statistical measures evidence-based medicine attempts to express clinical benefits of tests and treatments using mathematical methods. tools used by practitioners of evidence-based medicine include: likelihood ratio main article: likelihood ratios in diagnostic testing the pre-test odds of a particular diagnosis, multiplied by the likelihood ratio, determines the post-test odds. (odds can be calculated from, and then converted to, the probability.) this reflects bayes' theorem. the differences in likelihood ratio between clinical tests can be used to prioritize clinical tests according to their usefulness in a given clinical situation. auc-roc the area under the receiver operating characteristic curve (auc-roc) reflects the relationship between sensitivity and specificity for a given test. high-quality tests will have an auc-roc approaching 1, and high-quality publications about clinical tests will provide information about the auc-roc. cutoff values for positive and negative tests can influence specificity and sensitivity, but they do not affect auc-roc. number needed to treat (nnt)/number needed to harm (nnh). nnt and nnh are ways of expressing the effectiveness and safety, respectively, of interventions in a way that is clinically meaningful. nnt is the number of people who need to be treated in order to achieve the desired outcome (e.g. survival from cancer) in one patient. for example, if a treatment increases the chance of survival by 5%, then 20 people need to be treated in order for 1 additional patient to survive because of the treatment. the concept can also be applied to diagnostic tests. for example, if 1,339 women age 50–59 need to be invited for breast cancer screening over a ten-year period in order to prevent one woman from dying of breast cancer, then the nnt for being invited to breast cancer screening is 1339. quality of clinical trials evidence-based medicine attempts to objectively evaluate the quality of clinical research by critically assessing techniques reported by researchers in their publications. trial design considerations: high-quality studies have clearly defined eligibility criteria and have minimal missing data. generalizability considerations: studies may only be applicable to narrowly defined patient populations and may not be generalizable to other clinical contexts. follow-up: sufficient time for defined outcomes to occur can influence the prospective study outcomes and the statistical power of a study to detect differences between a treatment and control arm. power: a mathematical calculation can determine whether the number of patients is sufficient to detect a difference between treatment arms. a negative study may reflect a lack of benefit, or simply a lack of sufficient quantities of patients to detect a difference. limitations and criticism there are a number of limitations and criticisms of evidence-based medicine. two widely cited categorization schemes for the various published critiques of ebm include the three-fold division of straus and mcalister ("limitations universal to the practice of medicine, limitations unique to evidence-based medicine and misperceptions of evidence-based-medicine") and the five-point categorization of cohen, stavri and hersh (ebm is a poor philosophic basis for medicine, defines evidence too narrowly, is not evidence-based, is limited in usefulness when applied to individual patients, or reduces the autonomy of the doctor/patient relationship). in no particular order, some published objections include: research produced by ebm, such as from randomized controlled trials (rcts), may not be relevant for all treatment situations. research tends to focus on specific populations, but individual persons can vary substantially from population norms. because certain population segments have been historically under-researched (due to reasons such as race, gender, age, and co-morbid diseases), evidence from rcts may not be generalizable to those populations. thus, ebm applies to groups of people, but this should not preclude clinicians from using their personal experience in deciding how to treat each patient. one author advises that "the knowledge gained from clinical research does not directly answer the primary clinical question of what is best for the patient at hand" and suggests that evidence-based medicine should not discount the value of clinical experience. another author stated that "the practice of evidence-based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research." the theoretical ideal of ebm (that every narrow clinical question, of which hundreds of thousands can exist, would be answered by meta-analysis and systematic reviews of multiple rcts) faces the limitation that research (especially the rcts themselves) is expensive; thus, in reality, for the foreseeable future, the demand for ebm will always be much higher than the supply, and the