Association between trial registration and positive study findings: cross sectional study (Epidemiological Study of Randomized Trials—ESORT)
Bibliographic record
Abstract
Abstract Objective To assess whether randomised controlled trials (RCTs) that were registered were less likely to report positive study findings compared with RCTs that were not registered and whether the association varied by funding source. Design Cross sectional study. Study sample All primary RCTs published in December 2012 and indexed in PubMed by November 2013. Trial registration was determined based on the report of a trial registration number in published RCTs or the identification of the trial in a search of trial registries. Trials were separated into prospectively and retrospectively registered studies. Main outcome measure Association between trial registration and positive study findings. Results 1122 eligible RCTs were identified, of which 593 (52.9%) were registered and 529 (47.1%) were not registered. Overall, registration was marginally associated with positive study findings (adjusted risk ratio 0.87, 95% confidence interval 0.78 to 0.98), even with stratification as prospectively and retrospectively registered trials (0.87, 0.74 to 1.03 and 0.88, 0.78 to 1.00, respectively). The interaction term between overall registration and funding source was marginally statistically significant and relative risk estimates were imprecise (0.75, 0.63 to 0.89 for non-industry funded and 1.03, 0.79 to 1.36 for industry funded, P interaction=0.046). Furthermore, a statistically significant interaction was not maintained in sensitivity analyses. Within each stratum of funding source, relative risk estimates were also imprecise for the association between positive study findings and prospective and retrospective registration. Conclusion Among published RCTs, there was little evidence of a difference in positive study findings between registered and non-registered clinical trials, even with stratification by timing of registration. Relative risk estimates were imprecise in subgroups of non-industry and industry funded trials.
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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 | 0.419 | 0.693 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".