GRADE equity guidelines 3: considering health equity in GRADE guideline development: rating the certainty of synthesized evidence
Bibliographic record
Abstract
OBJECTIVES: The aim of this paper is to describe a conceptual framework for how to consider health equity in the Grading Recommendations Assessment and Development Evidence (GRADE) guideline development process. STUDY DESIGN AND SETTING: Consensus-based guidance developed by the GRADE working group members and other methodologists. RESULTS: We developed consensus-based guidance to help address health equity when rating the certainty of synthesized evidence (i.e., quality of evidence). When health inequity is determined to be a concern by stakeholders, we propose five methods for explicitly assessing health equity: (1) include health equity as an outcome; (2) consider patient-important outcomes relevant to health equity; (3) assess differences in the relative effect size of the treatment; (4) assess differences in baseline risk and the differing impacts on absolute effects; and (5) assess indirectness of evidence to disadvantaged populations and/or settings. CONCLUSION: The most important priority for research on health inequity and guidelines is to identify and document examples where health equity has been considered explicitly in guidelines. Although there is a weak scientific evidence base for assessing health equity, this should not discourage the explicit consideration of how guidelines and recommendations affect the most vulnerable members of society.
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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 | 0.345 | 0.668 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.019 | 0.015 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".