When applying GRADE, how do we decide the target of certainty of evidence rating?
Bibliographic record
Abstract
The Grades of Recommendation, Assessment, Development and Evaluation' (GRADE) offers a widely adopted, transparent and structured process for developing and presenting summaries of evidence, including the certainty of evidence, for systematic reviews and recommendations in healthcare. GRADE defined certainty of evidence as 'the extent of our confidence that the estimates of the effect are correct (in the context of systematic review), or are adequate to support a particular decision or recommendation (in the context of guideline)'. Realising the incoherence in the conceptualisation, the GRADE working group re-clarified the certainty of evidence as 'the certainty that a true effect lies on one side of a specified threshold, or within a chosen range'. Following the new concept, in the context of both systematic reviews and health technology assessments, it is desirable for GRADE users to specify the thresholds and clarify of which effect they are certain. To help GRADE users apply GRADE in accordance with the new conceptualisation, GRADE defines three levels of contextualisation: minimally, partially and fully contextualised approaches, and provides possible thresholds for each level of contextualisation. In this article, we will use a hypothetic systematic review to illustrate the application of the minimally and partially contextualised approaches, and discuss the application of a fully contextualised approach in deciding how we are rating our certainty (i.e.target of the rating of certainty of evidence).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".