Dental sleep-related conditions and the role of oral healthcare providers: A scoping review
Bibliographic record
Abstract
Dental sleep medicine as a discipline was first described about a quarter of a century ago. Snoring, obstructive sleep apnea, sleep bruxism, xerostomia, hypersalivation, gastroesophageal reflux disease, and orofacial pain were identified as dental sleep-related conditions. This scoping review aimed to: i) identify previously unidentified dental sleep-related conditions; and ii) identify the role of oral healthcare providers in the prevention, assessment, and management of dental sleep-related conditions in adults. A systematic literature search was conducted in PubMed, Embase.com, Web of Science, and Cochrane. Studies that reported an actual or likely role of oral healthcare providers in the prevention, assessment, and/or management of sleep-related conditions were included. Of the 273 included studies, 260 were on previously listed dental sleep-related conditions; the other 13 were on burning mouth syndrome. Burning mouth syndrome was therefore added to the list of dental sleep-related conditions for the first aim and categorized into sleep-related orofacial pain. For the second aim, the role of oral healthcare providers was found to be significant in the prevention, assessment, and management of obstructive sleep apnea and sleep bruxism; in the assessment and management of snoring, sleep-related orofacial pain, and oral dryness; and in the assessment of sleep-related gastroesophageal reflux condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".