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Then, applying user-defined rules, CINeMA assigns judgments at 3 levels (no concerns, some concerns, or major concerns) to each domain.
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The reviewer’s input is required at the study level for within-study bias and indirectness. It covers 6 domains: (i) within-study bias (referring to the impact of risk of bias in the included studies), (ii) reporting bias (referring to publication and other reporting bias), (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. CINeMA is broadly based on the GRADE framework, with several conceptual and semantic differences. Here we introduce the Confidence in Network Meta-Analysis (CINeMA) approach.
#It cinema grade software
Based on GRADE, 2 systems have been proposed to evaluate the credibility of results from network meta-analyses, but the complexity of the methods and lack of suitable software have limited their uptake. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach provides an assessment of the confidence in the results from systematic reviews and meta-analyses, and many organisations, including the World Health Organization, have adopted the GRADE approach. However, fewer than 1% of published network meta-analyses assess the credibility of their conclusions. Network meta-analysis has become an increasingly popular tool for developing treatment guidelines and making recommendations on clinical effectiveness and cost-effectiveness.