GRADE guidance 36: updates to GRADE's approach to addressing inconsistency
Bibliographic record
Abstract
Objectives To update previous Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance by addressing inconsistencies and interpreting subgroup analyses. Study Design and Setting Using an iterative process, we consulted with members of the GRADE working group through multiple rounds of written feedback and discussions at GRADE working group meetings. Results The guidance complements previous guidance with clarification in two areas: (1) assessing inconsistency and (2) assessing the credibility of possible effect modifiers that might explain inconsistency. Specifically, the guidance clarifies that inconsistency refers to variability in results, not in study characteristics; that inconsistency assessment for binary outcomes requires consideration of both relative and absolute effects; how to decide between narrower and broader questions in systematic reviews and guidelines; that, with the same evidence, ratings of inconsistency may differ depending on the target of certainty rating; and how GRADE inconsistency ratings relate to a statistical measure of inconsistency I 2 depending on the context in which one views results. The second part of the guidance illustrates, based on a worked example, the use of the instrument to assess the credibility of effect modification analyses. The guidance explains the stepwise process of moving from a subgroup analysis to assessing the credibility of effect modification and, if found credible, to subgroup-specific effect estimates and GRADE certainty ratings. Conclusion This updated guidance addresses specific conceptual and practical issues that systematic review authors frequently face when considering the degree of inconsistency in estimates of treatment effects across studies.
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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 | 0.244 | 0.596 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.046 | 0.026 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.023 | 0.013 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.045 | 0.024 |
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".