The following criteria were used for evaluating AUCs: 0.50: worse than chance 0.50 0.60: very poor 0.61 0.70: poor 0.71 0.80: moderate 0.81 0.90: good 0.91 1.00: excellent or perfect test. 4.3.3.3 Data synthesis for risk prediction rules Evidence reviews on risk prediction rules or risk prediction tool results were presented separately for discrimination and calibration. The discrimination data were analysed according to the principles of data synthesis for diagnostic accuracy studies as outlined in section 4.3.3.2.2. Calibration data such as ),if reported,were presented separately to the discrimination data. The results were presented for each study separately along with the quality rating for the study and modified GRADE assessment. 4.3.3.4 Data synthesis for qualitative study reviews The main findings for each included paper were identified and thematic analysis methods were used to synthesise this information into broad overarching themes which were summarised into the main review findings. The evidence was presented in the form of a narrative summary detailing the evidence from the relevant papers and how this informed the overall review finding plus a statement on the level of confidence for that review finding. Considerable limitations and issues around relevance were listed. A summary evidence table with the succinct summary statements for each review finding was produced including the associated quality assessment. 4.3.4 Appraising the quality of evidence by outcomes 4.3.4.1 Intervention reviews The evidence for outcomes from the included RCTs and, where appropriate, non-randomised intervention studies, were evaluated and presented using an adaptation of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) toolbox developed by the international GRADE working group (http://www.gradeworkinggroup.org/). The software ) developed by the GRADE working group was used to assess the quality of each outcome, taking into account individual study quality and the meta-analysis results. 