Shaping the research agenda for dental sleep-disordered breathing education in orthodontic residency programs
Bibliographic record
Abstract
Dental care providers are essential in screening and co-managing sleep-breathing disorders, particularly obstructive sleep apnea. As an integral component of dental medicine education, Dental Sleep-disordered Breathing Education (DSBE) aims to equip undergraduate and graduate dental students with the necessary knowledge, skills, and attitude to screen for and manage sleep-breading disorders as part of interdisciplinary teams. Studies on DSBE have mainly focused on undergraduate dental programs. Thus, research is needed to support the improvement of DSBE in dental residency programs, especially in orthodontics, to address the learning needs of future dental students, including Generation Z learners. This perspective paper suggests key research areas and methodologies to support this much-needed undertaking. These areas include curriculum mapping, outcome evaluation, and improvement/innovation. Dental researchers are encouraged to investigate these areas, employing the suggested methodologies. This will help overcome existing educational challenges and advance the available knowledge on DSBE in residency programs in orthodontics and dentistry at large.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".