Trends in sleep dentistry research in Asia: A bibliometric analysis
Bibliographic record
Abstract
Background: Sleep-related conditions such as obstructive sleep apnea and bruxism significantly affect both oral and systemic health, posing substantial public health challenges. Growing scholarly interest in sleep dentistry reflects an emerging effort to address these conditions through multidisciplinary research. This study employs bibliometric analysis to examine emerging themes, collaborative patterns, influential authors, and research trajectories related to sleep dentistry in Asia. Methods: A comprehensive search was conducted using the Scopus database to identify relevant publications from inception through April 2025. Bibliometric techniques were applied to analyze co-authorship networks, annual publication trends, institutional and international collaborations, keyword co-occurrence, and citation metrics. VOSviewer and the Bibliometrix package in R were utilized for data visualization and network mapping. Results: The analysis included 1,237 publications. China was the leading contributor, followed by the United States and India. The United Kingdom exhibited the highest ratio of Multiple Country Publications, followed by Australia and Canada. Tehran University of Medical Sciences emerged as the most productive institution, followed by the All India Institute of Medical Sciences and Shahid Beheshti University of Medical Sciences. Co-authorship analysis revealed six distinct collaborative clusters, with a total of 5,828 scholars contributing to the field. Conclusion: A substantial and growing body of research on sleep dentistry has emerged in Asia. The bibliometric findings highlight influential contributors, international cooperation, and key research themes particularly obstructive sleep apnea and bruxism underscoring the value of bibliometric methods in shaping responses to this pressing regional public health concern.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.136 | 0.213 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".