GRADE guidelines 32: GRADE offers guidance on choosing targets of GRADE certainty of evidence ratings
Bibliographic record
Abstract
OBJECTIVE: To provide practical principles and examples to help GRADE users make optimal choices regarding their ratings of certainty of evidence using a minimally or partially contextualized approach. STUDY DESIGN AND SETTING: Based on the GRADE clarification of certainty of evidence in 2017, a project group within the GRADE Working Group conducted iterative discussions and presentations at GRADE Working Group meetings to refine this construct and produce practical guidance. RESULTS: Systematic review and health technology assessment authors need to clarify what it is in which they are rating their certainty of evidence (i.e., the target of their certainty rating). The decision depends on the degree of contextualization (partially or minimally contextualized), thresholds (null, small, moderate or large effect threshold), and where the point estimate lies in relation to the chosen threshold(s). When the 95% confidence interval crosses multiple possible thresholds (i.e., including both large benefit and large harm), it is not worthwhile for authors to determine the target of certainty rating. CONCLUSION: GRADE provides practical principles to help systematic review and health technology assessment authors specify the target of their certainty of evidence rating.
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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 | 0.253 | 0.668 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.034 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.018 | 0.010 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.043 | 0.033 |
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".