GRADE Guidance 34: update on rating imprecision using a minimally contextualized approach
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to provide updated guidance on when The Grading of Recommendations Assessment, Development and Evaluation (GRADE) users should consider rating down more than one level for imprecision using a minimally contextualized approach. STUDY DESIGN AND SETTING: Based on the first GRADE guidance addressing imprecision rating in 2011, a project group within the GRADE Working Group conducted iterative discussions and presentations at GRADE Working Group meetings to produce this guidance. RESULTS: GRADE suggests aligning imprecision criterion for systematic reviews and guidelines using the approach that relies on thresholds and confidence intervals (CI) of absolute effects as a primary criterion for imprecision rating (i.e., CI approach). Based on the CI approach, when a CI appreciably crosses the threshold(s) of interest, one should consider rating down two or three levels. When the CI does not cross the threshold(s) and the relative effect is large, one should implement the optimal information size (OIS) approach. If the sample size of the meta-analysis is far less than the OIS, one should consider rating down more than one level for imprecision. CONCLUSION: GRADE provides updated guidance for imprecision rating in a minimally contextualized approach, with a focus on the circumstances in which one should seriously consider rating down two or three levels for imprecision.
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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 | 0.283 | 0.676 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.017 | 0.013 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".