Core GRADE 5: rating certainty of evidence—assessing indirectness
Bibliographic record
Abstract
This fifth article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to systematic reviews, clinical practice guidelines, and health technology assessments and addresses issues of indirect evidence. Guideline developers and health technology assessment practitioners must carefully specify the population, intervention, comparison, and outcome (PICO)—their target PICO—and consider the extent to which the best available evidence matches their target. When target and study PICOs differ substantially, studies provide indirect evidence and Core GRADE users may rate down the certainty of evidence as a result of this indirectness. Whether examining studies from a search for direct evidence or a deliberate search for indirect evidence, for each substantial difference between target and study PICO Core GRADE users must judge the likelihood that magnitude of effects will differ substantially. The greater the likelihood of substantial differences the more advisable rating down for indirectness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.235 | 0.624 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.023 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".