<scp>GRADE</scp> pearls and pitfalls—Part 1: Systematic reviews and meta‐analyses
Bibliographic record
Abstract
BACKGROUND: The Grading of Recommendation, Assessment, Development and Evaluation (GRADE) approach is used to assess the certainty of evidence in systematic reviews and meta-analyses. METHODS: We describe how the GRADE approach is used in systematic reviews and meta-analyses, including key points and examples. This overview is aimed at clinicians and researchers who are, or plan to be, involved in the development or assessment of systematic reviews with meta-analyses using GRADE. RESULTS: We outline how the certainty of evidence is assessed, how the evidence is summarized using GRADE evidence profiles or summary of findings tables, how the results are communicated, and we discuss challenges, advantages, and disadvantages with using GRADE. CONCLUSIONS: This overview aims to provide an overview of how GRADE is used in systematic reviews and meta-analyses, and may be used by systematic review developers, methodologists, and evidence end-users.
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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 | 0.214 | 0.711 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.029 | 0.038 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.049 | 0.019 |
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".