Certainty of evidence assessment in high‐impact medical journals: A meta‐epidemiological survey
Bibliographic record
Abstract
Introduction: While certainty of evidence assessment is key to a rigorous and transparent systematic review, it is unknown how - and how frequently - it is assessed in systematic reviews. The objective of this study was to examine the prevalence and approaches used for certainty of evidence assessment in systematic reviews published in high-impact medicine journals over the past 11 years. Methods: A PubMed search and hand-searching of relevant journal websites identified systematic reviews published between 24 January 2013 and 23 January 2024 in any of the ten highest-impact journals in the General and Internal Medicine category of the Journal Citation Report. Two reviewers independently selected any systematic review related to health outcomes assessing certainty of evidence using any method. We extracted data related to review characteristics, certainty of evidence and risk of bias/methodological quality assessment frameworks, and reported consideration of certainty of evidence domains. Logistic regression examined year of publication to determine whether the prevalence of certainty of evidence assessment changed over time. Results: < .001). Most (89.3%) of reviews used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework to assess certainty of evidence. Conclusion: Only one in three systematic reviews published in the highest-impact medical journals over the past 11 years assessed certainty of evidence, though prevalence increased over time. The use of specific domains within each certainty of evidence framework was not clearly described in all reviews.
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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 | 0.293 | 0.661 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".