Public availability of randomized clinical trial protocols: a repeated meta-research study
Bibliographic record
Abstract
OBJECTIVES: Making protocols of randomized clinical trials (RCTs) publicly available is important for the trustworthiness and quality of medical research. In a previous study assessing 326 RCTs with ethical approval in 2012, only 36% had a publicly available protocol. We aimed to generate current evidence on the availability of RCT protocols and to evaluate changes over time. STUDY DESIGN AND SETTING: Using a representative sample of RCTs approved in 2016 in Switzerland, Canada, Germany, and the United Kingdom, we investigated the number of available protocols by searching PubMed, Google Scholar, trial registries, and Google. Up to June 2024, we systematically searched for (i) protocols available as peer-reviewed publications, (ii) protocols attached to trial registries, and (iii) protocols shared with result publications of RCTs. We used multivariable logistic regression to examine the association of protocol availability with trial characteristics such as sample size, drug vs nondrug interventions, multicenter vs single-center status, and RCT approval in 2016 vs 2012. RESULTS: Of the 347 included RCTs, 228 (66%) had an available protocol. Forty-three percent (150/347) of the protocols were available as files on trial registries, 26% (91/347) as supplementary material to result publication, and 23% (81/347) as peer-reviewed publications. Protocol availability improved over time in industry trials (83.4% in 2016 vs 34.6% in 2012). Protocol availability for nonindustry trials remained low (46.4% 2016 vs 38.1% 2012). Multicenter trials (206/256; 77.7% vs single-center trials 22/82; 26.8%) and larger sample size (>500 participants 68/77; 88.3%, 100-500 participants 131/191; 68.6%, <100 participants 29/79; 36.7%) showed higher protocol availability. CONCLUSION: The availability of protocols increased in RCTs approved in 2016 compared to RCTs from 2012. This was mainly driven by industry sponsored trials. Efforts to further improve protocol availability should be continued, especially in nonindustry sponsored RCTs.
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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 | 0.633 | 0.838 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".