Identifying outcome reporting bias in randomised trials on PubMed: review of publications and survey of authors
Bibliographic record
Abstract
OBJECTIVE: To examine the extent and nature of outcome reporting bias in a broad cohort of published randomised trials. DESIGN: Retrospective review of publications and follow up survey of authors. Cohort All journal articles of randomised trials indexed in PubMed whose primary publication appeared in December 2000. MAIN OUTCOME MEASURES: Prevalence of incompletely reported outcomes per trial; reasons for not reporting outcomes; association between completeness of reporting and statistical significance. RESULTS: 519 trials with 553 publications and 10,557 outcomes were identified. Survey responders (response rate 69%) provided information on unreported outcomes but were often unreliable--for 32% of those who denied the existence of such outcomes there was evidence to the contrary in their publications. On average, over 20% of the outcomes measured in a parallel group trial were incompletely reported. Within a trial, such outcomes had a higher odds of being statistically non-significant compared with fully reported outcomes (odds ratio 2.0 (95% confidence interval 1.6 to 2.7) for efficacy outcomes; 1.9 (1.1 to 3.5) for harm outcomes). The most commonly reported reasons for omitting efficacy outcomes included space constraints, lack of clinical importance, and lack of statistical significance. CONCLUSIONS: Incomplete reporting of outcomes within published articles of randomised trials is common and is associated with statistical non-significance. The medical literature therefore represents a selective and biased subset of study outcomes, and trial protocols should be made publicly available.
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.
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 | 0.674 | 0.869 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.058 | 0.050 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".