Comparative assessment of the ROBINS-E, Newcastle-Ottawa and JBI in Medicine trials : a meta-research study
Bibliographic record
Abstract
Aim: The aim of this study was to compare the ROBINS-E (Risk of Bias in Non-Randomised Studies of Exposures) and Newcastle–Ottawa Scale (NOS) tools in assessing risk of bias in observational studies, analysing the differences in the classifications obtained and their implication in the interpretation of methodological quality. Methods: 50 observational studies published between January 2023 and January 2024 were selected from scientific databases such as PubMed/Medline, Scopus, and EMBASE. The results were compared with each other and, for each study, the impact factor, journal quartile, estimated sample size and adherence to STROBE guidelines were recorded. Results: A significant discrepancy was observed between the classifications obtained by the two tools. ROBINS-E classified a greater number of studies as High Risk (30%) than NOS (2%). Of the studies classified as Low Risk by NOS, 21,1% maintained their classification in ROBINS-E, 57,9% were downgraded to Some Concerns, and 21,1% were downgraded to High Risk. Of the studies classified as Moderate by NOS, none were classified as Low Risk by ROBINS-E, 46,7% remained as Some Concerns, 36,7% were downgraded to High Risk and 16,7% were downgraded to Very High Risk. The only study classified as High Risk by NOS was downgraded to Very High Risk. Conclusions: These results show that, although both tools share the same objective, they differ in their approach and depth of analysis. NOS proved to be practical and useful for an initial assessment, providing a comprehensive and objective overview of the quality of the studies, while ROBINS-E proved to be more rigorous and capable of identifying methodological flaws. The percentage of studies that were downgraded when assessed by ROBINS-E was notable, compared to the more favourable ratings assigned by NOS. This result highlights the greater sensitivity and rigour of ROBINS-E.
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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 | 0.282 | 0.451 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.069 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".