Research transparency in dental research: A programmatic analysis
Bibliographic record
Abstract
We assessed adherence to five transparency practices-data sharing, code sharing, conflict of interest disclosure, funding disclosure, and protocol registration-in articles in dental journals. We searched and exported the full text of all research articles from PubMed-indexed dental journals available in the Europe PubMed Central database until the end of 2021. We programmatically assessed their adherence to the five transparency practices using a validated and automated tool. Journal- and article-related information was retrieved from ScimagoJR and Journal Citation Reports. Of all 329,784 articles published in PubMed-indexed dental journals, 10,659 (3.2%) were available to download. Of those, 77% included a conflict of interest disclosure, and 62% included a funding disclosure. Seven percent of the articles had a registered protocol. Data sharing (2.0%) and code sharing (0.1%) were rarer. Sixteen percent of articles did not adhere to any of the five transparency practices, 29% adhered to one, 48% adhered to two, 7.0% adhered to three, 0.3% adhered to four, and no article adhered to all five practices. Adherence to transparency practices increased over time; however, data and code sharing especially remained rare. Coordinated efforts involving all stakeholders are needed to change current transparency practices in dental research.
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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 | 0.383 | 0.595 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.021 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".