Rapid, accurate publication and dissemination of clinical trial results: benefits and challenges
Bibliographic record
Abstract
Large-scale clinical trials are essential in cardiology and require rapid, accurate publication, and dissemination. Whereas conference presentations, press releases, and social media disseminate information quickly and often receive considerable coverage by mainstream and healthcare media, they lack detail, may emphasize selected data, and can be open to misinterpretation. Preprint servers speed access to research manuscripts while awaiting acceptance for publication by a journal, but these articles are not formally peer-reviewed and sometimes overstate the findings. Publication of trial results in a major journal is very demanding but the use of existing checklists can help accelerate the process. In case of rejection, procedures such as easing formatting requirements and possibly carrying over peer-review to other journals could speed resubmission. Secondary publications can help maximize benefits from clinical trials; publications of secondary endpoints and subgroup analyses further define treatment effects and the patient populations most likely to benefit. These rely on data access, and although data sharing is becoming more common, many challenges remain. Beyond publication in medical journals, there is a need for wider knowledge dissemination to maximize impact on clinical practice. This might be facilitated through plain language summary publications. Social media, websites, mainstream news outlets, and other publications, although not peer-reviewed, are important sources of medical information for both the public and for clinicians. This underscores the importance of ensuring that the information is understandable, accessible, balanced, and trustworthy. This report is based on discussions held on December 2021, at the 18th Global Cardiovascular Clinical Trialists meeting, involving a panel of editors of some of the top medical journals, as well as members of the lay press, industry, and clinical trialists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.746 | 0.901 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.059 | 0.060 |
| Open science | 0.011 | 0.029 |
| Research integrity | 0.024 | 0.036 |
| Insufficient payload (model declined to judge) | 0.026 | 0.045 |
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".