Spin in Randomized Controlled Trials in Obstetrics and Gynecology: A Systematic Review
Bibliographic record
Abstract
Objectives: The objective of this study was to evaluate the extent, type, and severity of spin in randomized controlled trials (RCTs) in obstetrics and gynecology. Data Sources: The top five highest impact journals in obstetrics and gynecology were systematically searched for RCTs with non-significant primary outcomes published between January 1, 2019, and December 31, 2020. Methods: Study selection and data extraction assessment were conducted independently and in duplicate. The extent, type, and severity of spin was identified and reported with previously established methodology, and risk of bias was assessed with the Cochrane Risk-of-Bias 2 Tool independently and in duplicate. Fisher's exact tests were used to evaluate the association between study characteristics, risk of bias, and spin. Results: We identified 1475 publications, of which 59 met our inclusion criteria. Articles evaluated interventions in obstetrics (n = 37, 63%) and gynecology (n = 22, 37%). Spin was not detected in 28 (47%) of the articles: Three (5%) had one, 10 (17%) had two, and 18 (31%) had greater than two occurrences of spin. Compared with articles where no spin was detected, spin was associated with the Cochrane Risk-of-Bias domain pertaining to missing data (p < 0.05). No association was observed with the journal, funding source, number of authors, types of interventions, and whether the study involved gynecology or obstetrics. Conclusions: Spin was detected in nearly half of 1:1 parallel two-arm RCTs in obstetrics and gynecology, highlighting the need for caution in the interpretation of RCT findings, particularly when the primary outcome is nonsignificant.
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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 | 0.522 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".