Publication Rates of Heart Failure Clinical Trials Remain Low
Bibliographic record
Abstract
BACKGROUND: Under-reporting of clinical trial results inhibits dissemination of knowledge, limits understanding of therapeutic interventions, and may ultimately harm patients. OBJECTIVES: This study examined the rates and predictors of heart failure clinical trial publication and how they have changed over time. METHODS: This study assessed cross-sectional analysis of all heart failure clinical trials registered on ClinicalTrials.gov with at least 2 years follow-up after trial completion. The content area was chosen for the robust clinical trial activity in the field. The primary outcome was manuscript publication with multivariable proportional hazards adjustment to identify associations with publication. RESULTS: Of the 1,429 included studies, 806 (56%) were published as manuscripts, 623 were unpublished, and 97 (7%) reported results without manuscript publication. Of the total, 1,243 were completed after 2007, when the mean 1-year publication rate for interventional trials rose from 12.7% to 19.6% (p = 0.049), which was possibly associated with changes in government regulation. However, there was no further sustained improvement over time, and there was no multivariable association between later completion dates and reporting or publication of results. Funding from the National Institutes of Health and use of clinical (death, hospitalization, myocardial infarction, changes in functional classification) rather than nonclinical primary endpoints were associated with earlier publication. Whether the results were consistent with the primary study hypothesis was not associated with likelihood of publication. CONCLUSIONS: The rates of heart failure clinical trial publication or reporting of results remain unacceptably low. Additional efforts by all stakeholders, including investigators, sponsors, regulators, societies, editors, and journals are needed to improve data dissemination.
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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 | 0.260 | 0.659 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".