Clinical trial registries are of minimal use for identifying selective outcome and analysis reporting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.805 | 0.955 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it