The face of Dental Sleep Medicine in the 21st century
Bibliographic record
Abstract
It becomes increasingly clear that some sleep disorders have important diagnostic and/or management links to the dental domain, hence the emergence of the discipline 'Dental Sleep Medicine'. In this review, the following topics are discussed: 1. the reciprocal associations between oro-facial pain and sleep; 2. the associations between sleep bruxism and other sleep-related disorders; 3. the role of the dentist in the assessment and management of sleep bruxism; and 4. the dental management of obstructive sleep apnoea. From these topics' descriptions, it becomes clear that the role of the dentist in the recognition and management of sleep-related oro-facial pain, sleep bruxism and obstructive sleep apnoea is large and important. Since many dental sleep disorders can have severe consequences for the individual's general health and well-being, it is imperative that dentists are not only willing to take on that role, but are also able to do so. This requires more attention for Dental Sleep Medicine in the dental curricula worldwide, as well as better postgraduate training of dentists who are interested in specialising in this intriguing domain. This review contributes to increasing the dental researcher's, teacher's and care professional's insight into the discipline 'Dental Sleep Medicine' as it has taken shape in the 21st century, to the benefit of all patients suffering from dental sleep disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".