Core GRADE 4: rating certainty of evidence—risk of bias, publication bias, and reasons for rating up certainty
Bibliographic record
Abstract
This fourth article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to addressing risk of bias, publication bias, and rating up certainty. In Core GRADE, randomised controlled trials begin as high certainty evidence and non-randomised studies of interventions (NRSI) as low certainty. To assess certainty of evidence for risk of bias, Core GRADE users first classify individual studies as low or high risk of bias. Decisions regarding rating down for risk of bias will depend on the weights of high and low risk of bias studies and similarities or differences between the results of high and low risk of bias studies. For publication bias, a body of evidence comprising small studies funded by industry should raise suspicion. Core GRADE users appraising results from well conducted NSRI can consider rating up certainty of evidence when risk ratios from pooled estimates suggest large or very large effects.
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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 | 0.279 | 0.700 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 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".