Clinical trial registries are of minimal use for identifying selective outcome and analysis reporting
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine selective outcome reporting (SOR) and selective analysis reporting (SAR) in randomized controlled trials (RCTs) and to explore the usefulness of trial registries for identifying SOR and SAR. STUDY DESIGN AND SETTING: We selected one "index outcome" for each of three comparative effectiveness reviews (CERs) of pharmacotherapy and extracted data on this outcome from trial registries and from study publications. RESULTS: Among 50 RCTs published since 2005 and reporting the index outcome, only 50% were listed in registries; 90% of RCTs were assessed as having SOR or SAR. The index outcome in the registry was different from that in the publication in 75% of trials in two CERs, and not specified at all in the third. Reported outcomes and analyses were not consistent between the publication's methods section and the results section in 33% and 46% of the two CERs where the index outcome was a benefit. There were no statistically significant predictors of SOR and SAR in our small sample where some predictors lacked variability. CONCLUSION: The SOR and SAR were frequent in this pilot study, and the most common type of SOR was the publication of outcomes that were not pre-specified. Trial registries were of little use in identifying SOR and of no use in identifying SAR.
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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 | 0.880 | 0.949 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.017 | 0.009 |
| Bibliometrics | 0.038 | 0.041 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".