Published intimate partner violence studies often differ from their trial registration records
Bibliographic record
Abstract
INTRODUCTION: Registering study protocols in a trial registry is important for methodologic transparency and reducing selective reporting bias. The objective of this investigation was to determine whether published studies of intimate partner violence (IPV) that had been registered matched the registration record on key study design elements. METHODS: We systematically searched three trial registries to identify registered IPV studies and the published literature for the associated publication. Two authors independently determined for each study whether key study elements in the registry matched those in the published paper. RESULTS: We included 66 studies published between 2006 and 2017. Nearly half (29/66, 44%) were registered after study completion. Many (26/66, 39%) had discrepancies regarding the primary outcome, and nearly two-thirds (42/66, 64%) had discrepancies in secondary outcomes. Discrepancies in study design were less frequent (13/66, 20%). However, large changes in sample size (26/66, 39%) and discrepancies in funding source (28/66, 42%) were frequently observed. CONCLUSIONS: Trial registries are important tools for research transparency and identifying and preventing outcome switching and selective outcome reporting bias. Published IPV studies often differ from their records in trial registries. Researchers should pay close attention to the accuracy of trial registry records.
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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 | 0.660 | 0.885 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".