Core GRADE 3: rating certainty of evidence—assessing inconsistency
Bibliographic record
Abstract
This third article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to deciding whether to rate down certainty of evidence due to inconsistency—that is, unexplained variability in results across studies. For binary outcomes in which relative effects are consistent across baseline risks while absolute effects are not, Core Grade users assess consistency in relative effects. For continuous outcomes, they assess consistency in the absolute effects. When planning for the possibility of inconsistent results across studies, systematic review authors using Core GRADE construct a priori hypotheses regarding population or intervention characteristics that may explain inconsistency. They then judge the magnitude of inconsistency by considering the extent to which point estimates differ and the degree to which confidence intervals overlap. Before making a decision on rating down, Core GRADE users will evaluate where individual study estimates lie in relation to the threshold of the certainty rating (minimal important difference or the null). Finally, they will test their subgroup hypothesis and if an effect proves credible will provide separate evidence summaries and rate certainty of evidence separately for each subgroup. When they find no credible subgroup effect, they will provide a single evidence summary, rating down for inconsistency if necessary.
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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 | 0.201 | 0.625 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".