The GRADE Working Group clarifies the construct of certainty of evidence
Bibliographic record
Abstract
OBJECTIVE: To clarify the grading of recommendations assessment, development and evaluation (GRADE) definition of certainty of evidence and suggest possible approaches to rating certainty of the evidence for systematic reviews, health technology assessments, and guidelines. STUDY DESIGN AND SETTING: This work was carried out by a project group within the GRADE Working Group, through brainstorming and iterative refinement of ideas, using input from workshops, presentations, and discussions at GRADE Working Group meetings to produce this document, which constitutes official GRADE guidance. RESULTS: Certainty of evidence is best considered as the certainty that a true effect lies on one side of a specified threshold or within a chosen range. We define possible approaches for choosing threshold or range. For guidelines, what we call a fully contextualized approach requires simultaneously considering all critical outcomes and their relative value. Less-contextualized approaches, more appropriate for systematic reviews and health technology assessments, include using specified ranges of magnitude of effect, for example, ranges of what we might consider no effect, trivial, small, moderate, or large effects. CONCLUSION: It is desirable for systematic review authors, guideline panelists, and health technology assessors to specify the threshold or ranges they are using when rating the certainty in evidence.
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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 | 0.468 | 0.775 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.037 | 0.016 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.019 | 0.023 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".