Cross-Sectional Evaluation of Open Science Practices at Imaging Journals: A Meta-Research Study
Bibliographic record
Abstract
Objective: To evaluate open science policies of imaging journals, and compliance to these policies in published articles. Methods: From imaging journals listed we extracted open science policy details: protocol registration, reporting guidelines, funding, ethics and conflicts of interest (COI), data sharing, and open access publishing. The 10 most recently published studies from each journal were assessed to determine adherence to these policies. We calculated the proportion of open science policies into an Open Science Score (OSS) for all journals and articles. We evaluated relationships between OSS and journal/article level variables. Results: 82 journals/820 articles were included. The OSS of journals and articles was 58.3% and 31.8%, respectively. Of the journals, 65.9% had registration and 78.1% had reporting guideline policies. 79.3% of journals were members of COPE, 81.7% had plagiarism policies, 100% required disclosure of funding, and 97.6% required disclosure of COI and ethics approval. 81.7% had data sharing policies and 15.9% were fully open access. 7.8% of articles had a registered protocol, 8.4% followed a reporting guideline, 77.4% disclosed funding, 88.7% disclosed COI, and 85.6% reported ethics approval. 12.3% of articles shared their data. 51% of articles were available through open access or as a preprint. OSS was higher for journal with DOAJ membership (80% vs 54.2%; P < .0001). Impact factor was not correlated with journal OSS. Knowledge synthesis articles has a higher OSS scores (44.5%) than prospective/retrospective studies (32.6%, 30.0%, P < .0001). Conclusion: Imaging journals endorsed just over half of open science practices considered; however, the application of these practices at the article level was lower.
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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 | 0.066 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".