GRADE guidance 24 optimizing the integration of randomized and non-randomized studies of interventions in evidence syntheses and health guidelines
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This is the 24th in the ongoing series of articles describing the GRADE approach for assessing the certainty of a body of evidence in systematic reviews and health technology assessments and how to move from evidence to recommendations in guidelines. METHODS: Guideline developers and authors of systematic reviews and other evidence syntheses use randomized controlled studies (RCTs) and non-randomized studies of interventions (NRSI) as sources of evidence for questions about health interventions. RCTs with low risk of bias are the most trustworthy source of evidence for estimating relative effects of interventions because of protection against confounding and other biases. However, in several instances, NRSI can still provide valuable information as complementary, sequential, or replacement evidence for RCTs. RESULTS: In this article we offer guidance on the decision regarding when to search for and include either or both types of studies in systematic reviews to inform health recommendations. CONCLUSION: This work aims to help methodologists in review teams, technology assessors, guideline panelists, and anyone conducting evidence syntheses using GRADE.
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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 | 0.337 | 0.738 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".