Discontinuation and Non-Publication of Heart Failure Randomized Controlled Trials: A Call to Publish All Trial Results
Bibliographic record
Abstract
AIMS: Discontinuation or non-publication of trials may hinder scientific progress and violates the commitment made to research participants. We sought to identify the prevalence of discontinuation and non-publication of heart failure (HF) clinical trials. METHODS AND RESULTS: We conducted a cross-sectional search of ClinicalTrials.gov to identify all completed and discontinued HF clinical trials. We limited our search to only include trials that were completed by 31 December 2017. Trials were investigated to identify reasons for discontinuation. Informative termination was defined as trial termination due to safety or efficacy concerns. Data pertaining to the trial phase, funding, intervention, enrolment, and trial completion date were extracted for each trial. A total of 572 trials were included. Of these, 21% (n = 118) were discontinued before completion. Patient accrual was the most frequently cited reason (n = 42; 36%) for trial discontinuation, followed by informative termination (n = 16; 14%) and funding (n = 14; 12%). Overall, 24 780 patients were enrolled in trials that were terminated. Of trials that were completed and not terminated, nearly one-third (n = 131/454; 29%) were not published. Seventy-nine (24%) trials were published within 12 months, 192 (59%) within 24 months, and 252 (78%) trials within 36 months. CONCLUSIONS: Discontinuation and non-publication of HF trials is common. This raises ethical concerns towards participants who volunteer for research and are exposed to potential risks, inconvenience, and discomfort without furthering scientific progress.
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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 | 0.798 | 0.857 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".