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This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can rate the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA) on the domains imprecision, inconsistency, publication bias, and other domains. It also provides guidance for how to present synthesized information in evidence profiles and summary of findings tables. We present guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Supplemented by practical examples, we describe how raters of the evidence can apply the GRADE domains inconsistency, imprecision, and publication bias to a body of evidence of TA studies. Using GRADE in Cochrane and other reviews as well as World Health Organization and other guidelines helped refining the GRADE approach for rating the certainty of a body of evidence from TA studies. Although several of the GRADE domains (e.g., imprecision and magnitude of the association) require further methodological research to help operationalize them, judgments need to be made on the basis of what is known so far.

This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can assess the results and the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA). We present an overview of the GRADE approach and guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Part 1 of the two parts in this 21st guidance article about how to apply GRADE focuses on understanding study design issues in test accuracy, provide an overview of the domains, and describe risk of bias and indirectness specifically. Supplemented by practical examples, we describe how raters of the evidence using GRADE can evaluate study designs focusing on tests and how they apply the GRADE domains risk of bias and indirectness to a body of evidence of TA studies. Rating the certainty of a body of evidence using GRADE in Cochrane and other reviews and World Health Organization and other guidelines dealing with in TA studies helped refining our approach. The resulting guidance will help applying GRADE successfully for questions and recommendations focusing on tests.

This article describes GRADE guidance for assessing imprecision when rating the certainty of the evidence from network meta-analysis. A project group within the GRADE working group conducted iterative discussions, computer simulations, and presentations at GRADE working group meetings to produce and obtain approval for this guidance. When addressing imprecision of a network estimate, reviewers should consider the 95% confidence or credible interval, and the optimal information size. If the 95% confidence or credible interval crosses a pre-specified threshold, reviewers should rate down the certainty of the evidence. If the 95% confidence interval does not cross any pre-specfied threshold, reviewers should consider the optimal information size. Because addressing the optimal information size may be challenging, reviewers can use the effect size to decide if any calculations are necessary. When the size of the effect is modest or the optimal information size is met, reviewers should not rate down for imprecision. Reviewers should use this guidance when to appropriately address imprecision in the context of the assessment of certainty of evidence from network meta-analysis.

The objective of this study was to provide guidance on the use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to determine certainty in estimates of association between prognostic factors and future outcomes. We developed our guidance through an iterative process that involved review of published systematic reviews and meta-analyses of prognostic factors, consultation with members, feedback, presentation, and discussion at the GRADE Working Group meetings. For questions of prognosis, a body of observational evidence (potentially including patients enrolled in randomized controlled trials) begins as high certainty in the evidence. The five domains of GRADE for rating down certainty in the evidence, that is, risk of bias, imprecision, inconsistency, indirectness, and publication bias, as well as the domains for rating up, also apply to estimates of associations between prognostic factors and outcomes. One should determine if their ratings do not consider (noncontextualized) or consider (contextualized) the clinical context as this will may result in variable judgments on certainty of the evidence. The same principles GRADE proposed for bodies of evidence addressing treatment and overall prognosis work well in assessing individual prognostic factors, both in noncontextualized and contextualized settings.

The Grading of Recommendations, Assessment, Development and Evaluations (GRADE)-ADOLOPMENT methodology has been widely used to adopt, adapt, or de novo develop recommendations from existing or new guideline and evidence synthesis efforts. The objective of this guidance is to refine the operationalization for applying GRADE-ADOLOPMENT. Through iterative discussions, online meetings, and email communications, the GRADE-ADOLOPMENT project group drafted the updated guidance. We then conducted a review of handbooks of guideline-producing organizations, and a scoping review of published and planned adolopment guideline projects. The lead authors refined the existing approach based on the scoping review findings and feedback from members of the GRADE working group. We presented the revised approach to the group in November 2022 (approximately 115 people), in May 2023 (approximately 100 people), and twice in September 2023 (approximately 60 and 90 people) for approval. This GRADE guidance shows how to effectively and efficiently contextualize recommendations using the GRADE-ADOLOPMENT approach by doing the following: (1) showcasing alternative pathways for starting an adolopment effort; (2) elaborating on the different essential steps of this approach, such as building on existing evidence-to-decision (EtDs), when available or developing new EtDs, if necessary; and (3) providing examples from adolopment case studies to facilitate the application of the approach. We demonstrate how to use contextual evidence to make judgments about EtD criteria, and highlight the importance of making the resulting EtDs available to facilitate adolopment efforts by others. This updated GRADE guidance further operationalizes the application of GRADE-ADOLOPMENT based on over 6 years of experience. It serves to support uptake and application by end users interested in contextualizing recommendations to a local setting or specific reality in a short period of time or with limited resources.

The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose–response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either “off-the-shelf” or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care–related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework has become the global standard for rating evidence certainty and grading strength of health-care recommendations in systematic reviews, clinical practice guidelines (CPGs), and health technology assessments (HTAs). However, as the methodology has evolved, its growing complexity and difficulties navigating the many articles providing GRADE guidance have created challenges for users. Moreover, GRADE guidance has been published across multiple journals and platforms, resulting in a body of work with considerable redundancy, modifications, and out-of-date material that requires meticulous appraisal of GRADE writings to identify and implement current guidance. In response, a group of GRADE methodologists developed Core GRADE, a simple yet comprehensive framework focused on the essential elements required to apply GRADE to paired comparisons of interventions. Building on prior GRADE publications, the authors reviewed existing guidance and distilled its fundamental components. This proposal represents an independent approach and has not received formal endorsement from the GRADE working group. During this process, they identified multiple areas where clearer recommendations were warranted and incorporated these improvements into the seven Core GRADE papers. This article presents the resulting innovations introduced by core GRADE, that include the use of flow charts and algorithms to guide GRADE implementation; an emphasis on viewing both individual GRADE domains and overall certainty as continua; and clarification of decisions related to addressing potential relative and absolute subgroup effects when formulating patient population, intervention, comparison, and outcome questions. The GRADE framework was introduced in 2004 to help researchers and health-care professionals assess the certainty (quality) of evidence in systematic reviews and the strength of recommendations in CPGs and HTAs. Over the past 2 decades, it has become overwhelmingly the most widely used approach to making certainty of evidence and strength of recommendation decisions. However, many users now find GRADE increasingly complex, and guidance has appeared in numerous journals and platforms. This has created overlap, outdated information, and inconsistencies that make it difficult to identify the most current best approaches. To address these challenges, GRADE experts developed Core GRADE, a simplified but comprehensive version of GRADE. Core GRADE brings together the essential elements needed to apply GRADE when comparing health-care interventions (1 intervention vs 1 comparator). In addition to this simplification, Core GRADE offers several innovations. These include practical flowcharts and algorithms to guide step-by-step application of the framework; a new emphasis on viewing both individual domains and overall certainty of evidence as continua; and clearer advice on how to address different subpopulations when formulating research questions. Beyond simplification, these innovations further enhance GRADE's usefulness and ease of use. [Display omitted] •Core GRADE distills essential GRADE steps for single-intervention comparisons.•Development of algorithms to enhance consistency in GRADE application.•Continuum-based approach for rating domains and overall certainty of evidence.•Refined approach to addressing subgroup effects in PICO question development.

This seventh article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach for moving from evidence to recommendations or policy decisions. Core GRADE users make strong recommendations for an intervention versus a comparator when the desirable consequences clearly outweigh the undesirable consequences, and a conditional (weak) recommendation when the balance is less clear. Primary considerations in deciding on recommendations considering an individual patient perspective include balance of benefits, harms, and burdens; the certainty of evidence; and values and preferences. Secondary considerations, most important from a population perspective, include costs, feasibility, acceptability, and equity. Moving from evidence to recommendations begins with considering evidence regarding patients’ values and preferences and choosing the smallest difference in each outcome that patients perceive as important (the minimal important difference). Core GRADE users construct statements that make clear the values and preferences underlying their recommendations. In general, Core GRADE users make strong recommendations only when certainty of evidence is high or moderate. When evidence certainty is low, recommendations will be conditional under all but special circumstances

This fourth article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to addressing risk of bias, publication bias, and rating up certainty. In Core GRADE, randomised controlled trials begin as high certainty evidence and non-randomised studies of interventions (NRSI) as low certainty. To assess certainty of evidence for risk of bias, Core GRADE users first classify individual studies as low or high risk of bias. Decisions regarding rating down for risk of bias will depend on the weights of high and low risk of bias studies and similarities or differences between the results of high and low risk of bias studies. For publication bias, a body of evidence comprising small studies funded by industry should raise suspicion. Core GRADE users appraising results from well conducted NSRI can consider rating up certainty of evidence when risk ratios from pooled estimates suggest large or very large effects.

When direct and indirect estimates of treatment effects are coherent, network meta-analysis (NMA) estimates should have increased precision (narrower confidence or credible intervals compared with relying on direct estimates alone), a benefit of NMA. We have, however, observed cases of sparse networks in which combining direct and indirect estimates results in marked widening of the confidence intervals. In many cases, the assumption of common between-study heterogeneity across the network seems to be responsible for this counterintuitive result. Although the assumption of common between-study heterogeneity across paired comparisons may, in many cases, not be appropriate, it is required to ensure the feasibility of estimating NMA treatment effects. This is especially the case in sparse networks, in which data are insufficient to reliably estimate different variances across the network. The result, however, may be spuriously wide confidence intervals for some of the comparisons in the network (and, in the Grading of Recommendations Assessment, Development, and Evaluation approach, inappropriately low ratings of the certainty of the evidence through rating down for serious imprecision). Systematic reviewers should be aware of the problem and plan sensitivity analyses that produce intuitively sensible confidence intervals. These sensitivity analyses may include using informative priors for the between-study heterogeneity parameter in the Bayesian framework and the use of fixed effects models.