Research Transparency in 59 Disciplines of Clinical Medicine: A Meta-Research Study
Bibliographic record
Abstract
Background: Transparency in health research is crucial as it allows for the scrutiny and replication of findings, fosters confidence in scientific outcomes, and ultimately contributes to the advancement of knowledge and the betterment of society.Aim: We aimed to assess five transparency practices in scientific publications (data availability, code availability, protocol registration, conflicts of interest (COI) and funding disclosures) from open-access articles published in medical journals.Methods: We searched and exported all open-access articles from Science Citation Index Expanded (SCIE)-indexed journals through the Europe PubMed Central database published until March 16, 2024. Basic journal- and article-related information was retrieved from the database. We then assessed five transparency practices in the articles using the rtransparent package in R.Results: The analysis included 2,002,955 open-access articles from SCIE-indexed medical journals (open-access percentage=59.0%). Of these, 87.5% (95% CI: 87.4%-87.5%) disclosed COI and 80.1% (95% CI: 80.0%-80.1%) disclosed funding. Protocol registration was declared in 6.6% (95% CI: 6.6%-6.6%), data sharing in 7.6% (95% CI: 7.6%-7.6%), and code sharing in 1.4% (95% CI: 1.4%-1.4%) of the articles. More than 76.0% declared at least two transparency practices, while all five practices were declared in less than 0.02%. The data showed an increasing trend in all transparency practices since the late 2000s. Articles published in journals with higher impact factors and articles receiving more citations had increased odds of COI and funding disclosures, as well as data and code sharing. There were notable differences in transparency practices across the disciplines.Conclusion: While most articles had COI and funding disclosures, adherence to other transparency practices was grossly insufficient. To increase protocol registration, data, and code sharing, much stronger incentives and mandates are needed from all stakeholders.
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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 | 0.298 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".