Reproducibility Policies in Cardiology Journals: The REPLICA Cross-Sectional Study
Bibliographic record
Abstract
Abstract Importance Transparency and data sharing are valuable practices in research, contributing to improved precision and flexibility in cumulative evidence; and ultimately expanding the research ecosystem by addressing one of the philosophical research norms that implies that knowledge belongs to society. Objectives The objective of the Reproducibility Policies In Cardiology Journals (REPLICA) study was to estimate the proportions of policies and guidance for reproducibility and transparency practices among Cardiology journals, as well as to determine details of completeness of reporting and data sharing conditions whenever disclosed. Design Cross-sectional analysis. Setting Cross-sectional study through analyses of journals deposited in the National Library of Medicine (NLM) Catalog tagged with the “ Cardiology ” and “ Vascular Diseases ” entry terms. Eligibility Criteria Cardiology journals from the NLM Catalog database that published at least one randomized clinical trial in 2018. Journals that published articles in English, Spanish, French, or Portuguese and were available in MEDLINE/PubMed were eligible for inclusion. Exposures The exposures were mainly related to journal’s characteristics such as publisher operations characteristics (e.g., journal access only by subscription), indexation in the DOAJPlus, requirement for registration for RCTs, and others. Main outcomes and measures We prespecified a primary composite outcome composed of data-sharing policy or guidance. Secondary outcomes were proportions of reporting guidelines within the journal’s instructions for the author’s section (e.g., CONSORT), and also other components of sharing practices. Results We assessed 148 journals. Of them, 74 (50.0%, 95%CI 41.9% to 58.1%) presented policy or guidance for data sharing. We found guidance for data sharing in 68 journals (47.5% 95%CI 39.4% to 55.8%). Notably, among them, only two mentioned sharing individual participant data (IPD). Regarding guidelines for article reporting, we identified that 132 journals displayed guidance for authors, in which 27 (20.45%, 95%CI 14.34% to 28.29%) had CONSORT and EQUATOR Network guidance content. Conclusion and relevance In summary, we found a mild proportion of policies and guidance for data-sharing. Moreover, transparency practices inclined to RCTs are suboptimal, as mirrored by the very low prevalence of IPD data-sharing policy and guidance as well as specific reporting guidelines instructions for RCTs. Key Points Question What is the proportion of journals displaying policies and guidance about data sharing in cardiology journals? Findings We found a low prevalence of policy and guidance for data sharing in Cardiology journals, as well as transparency and reproducibility practices; details, individual participant data sharing, registration, and completeness of reporting, for example. Meaning Journals play a role in driving reproducibility and transparency among scientific areas. Stakeholders involved in the editorial processes should be open to understand the valuable impact of data-sharing practices and learn how to implement such mechanisms, that being the case.
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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 | 0.177 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".