Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research
Bibliographic record
Abstract
BACKGROUND: The reporting of outcomes within published randomized trials has previously been shown to be incomplete, biased and inconsistent with study protocols. We sought to determine whether outcome reporting bias would be present in a cohort of government-funded trials subjected to rigorous peer review. METHODS: We compared protocols for randomized trials approved for funding by the Canadian Institutes of Health Research (formerly the Medical Research Council of Canada) from 1990 to 1998 with subsequent reports of the trials identified in journal publications. Characteristics of reported and unreported outcomes were recorded from the protocols and publications. Incompletely reported outcomes were defined as those with insufficient data provided in publications for inclusion in meta-analyses. An overall odds ratio measuring the association between completeness of reporting and statistical significance was calculated stratified by trial. Finally, primary outcomes specified in trial protocols were compared with those reported in publications. RESULTS: We identified 48 trials with 68 publications and 1402 outcomes. The median number of participants per trial was 299, and 44% of the trials were published in general medical journals. A median of 31% (10th-90th percentile range 5%-67%) of outcomes measured to assess the efficacy of an intervention (efficacy outcomes) and 59% (0%-100%) of those measured to assess the harm of an intervention (harm outcomes) per trial were incompletely reported. Statistically significant efficacy outcomes had a higher odds than nonsignificant efficacy outcomes of being fully reported (odds ratio 2.7; 95% confidence interval 1.5-5.0). Primary outcomes differed between protocols and publications for 40% of the trials. INTERPRETATION: Selective reporting of outcomes frequently occurs in publications of high-quality government-funded trials.
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 | 0.776 | 0.902 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.027 | 0.043 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".