Are open science practices in dentistry associated with higher Altmetric scores and citation rates?
Bibliographic record
Abstract
AIM: Open science, a set of principles and practices, aims to make scientific research more accessible and accountable, benefiting scientists and society. This study evaluated whether adopting open science practices (OSPs) correlates with higher citation rates and Altmetric scores. METHODS: A random sample of randomised clinical trials (RCTs) on dental caries published between 2000 and 2022 was selected. A systematic PubMed search identified relevant RCTs, and data on OSPs - study registration, open methodology, open software, open scripts, open analysis plan, open data, open peer review, and open access (OA) - were manually collected by two independent assessors. The Robot Reviewer tool automatically evaluated the risk of bias (RoB). Outcomes included the total number of citations and the Altmetric Attention Score. Associations between OSPs, RoB, and other explanatory variables with the outcomes were assessed using binomial negative regression analysis, and expressed as Incidence Rate Ratio (IRR; α =0.05). RESULTS: In total, 323 papers were analysed. At least one OSP was adopted in 57.5 % (n = 186) of the articles, dropping to 39.6 % (n = 128) without OA. Papers with protocol registration (IRR: 1.45; 95 % CI: 1.15, 1.82) and OA publication (IRR: 1.24; 95 % CI: 1.01, 1.53) had higher citation rates. Conversely, papers in full OA journals had fewer citations (IRR: 0.67; 95 % CI: 0.52, 0.87). After adjusting for RoB, low-risk studies showed higher citation rates (IRR: 1.48; 95 % CI: 1.14, 1.91), while OA lost significance. For Altmetric scores, registered and OA manuscripts showed higher scores (IRR: 3.74; 95 % CI: 2.00, 7.01; IRR: 1.69; 95 % CI: 1.04, 2.75), with registration remaining significant after adjusting for RoB and impact factor (IRR: 3.71; 95 % CI: 1.97-6.99). CONCLUSION: The adoption of OSPs demonstrated a partial correlation with citation rates and Altmetric scores in RCTs on dental caries; however, these effects are complex and seem more related to the journal's impact factor. CLINICAL SIGNIFICANCE: The citations and the attention to clinical trials in dentistry, which could drive clinical decision-making and the elaboration of policies and recommendations, seem to be driven more by the journal's prestige than by the adoption of OSPs.
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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 | 0.146 | 0.532 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".