Grading the Strength of a Body of Evidence When Assessing Health Care Interventions for the Effective Health Care Program of the Agency for Healthcare Research and Quality: An Update
Bibliographic record
Abstract
Systematic reviews are essential tools for summarizing information to help users make well-informed decisions about health care options. The Evidence-based Practice Center (EPC) program, supported by the Agency for Healthcare Research and Quality (AHRQ), produces substantial numbers of such reviews, including those that explicitly compare two or more clinical interventions (sometimes termed comparative effectiveness reviews). These reports synthesize a body of literature; the ultimate goal is to help clinicians, policymakers, and patients make well-considered decisions about health care. The goal of strength of evidence assessments is to provide clearly explained, well-reasoned judgments about reviewers’ confidence in their systematic review conclusions so that decisionmakers can use them effectively.Beginning in 2007, AHRQ supported a cross-EPC set of work groups to develop guidance on major elements of designing, conducting, and reporting systematic reviews. Together the materials form the EPC Methods Guide for Effectiveness and Comparative Effectiveness Reviews; one chapter focused on grading the strength of evidence. This chapter updates the original EPC strength of evidence approach, presenting findings and recommendations of a work group with experience in applying previous guidance; it should be considered current guidance for EPCs. The guidance applies primarily to systematic reviews of drugs, devices, and other preventive and therapeutic interventions; it may apply to exposures (characteristics or risk factors that are determinants of health outcomes) and broader health services research questions. It does not address reviews of medical tests.EPC reports support the work of many decisionmakers, but EPCs do not themselves develop recommendations or practice guidelines. In particular, we limit our grading strength of evidence approach to individual outcomes. Unlike grading systems that were designed to be used more directly by specific decisionmakers,– we do not develop global summary judgments of the relative benefits and harms of treatment comparisons.We briefly explore the rationale for grading strength of evidence, define domains of concern, and describe our recommended grading system for systematic reviews. The aims of this guidance are twofold: (1) to foster appropriate consistency and transparency in the methods that different EPCs use to grade strength of evidence and (2) to facilitate users’ interpretations of those grades for guideline development or other decisionmaking tasks. Because this field is rapidly evolving, future revisions are anticipated; they will reflect our increasing understanding and experience with the methodology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.262 | 0.600 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.067 | 0.047 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".