Promoting public access to clinical trial protocols: challenges and recommendations
Bibliographic record
Abstract
BACKGROUND: Recognizing the value of promoting public access to clinical trial protocols, Trials pioneered the way for their publication over a decade ago. However, despite major advances in the public accessibility of information about trial methods and results, protocol sharing remains relatively rare. MAIN BODY: Protocol sharing facilitates the critical appraisal of clinical trials and helps to identify and deter the selective reporting of outcomes and analyses. Challenges to the routine availability of high quality trial protocols include the gaps in incentives and adherence mechanisms, limited venues for sharing the original and final protocol versions, and the need for mechanisms to ensure transparent and complete protocol content. CONCLUSIONS: We propose recommendations for addressing key challenges to protocol sharing in order to promote routine public access to protocols for the benefit of patients and other users of evidence from clinical 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 | 0.159 | 0.447 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.093 | 0.112 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".