Craniofacial Sleep Medicine: The Important Role of Dental Providers in Detecting and Treating Sleep Disordered Breathing in Children
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a clinical disorder within the spectrum of sleep-related breathing disorders (SRDB) which is used to describe abnormal breathing during sleep resulting in gas exchange abnormalities and/or sleep disruption. OSA is a highly prevalent disorder with associated sequelae across multiple physical domains, overlapping with other chronic diseases, affecting development in children as well as increased health care utilization. More precise and personalized approaches are required to treat the complex constellation of symptoms with its associated comorbidities since not all children are cured by surgery (removal of the adenoids and tonsils). Given that dentists manage the teeth throughout the lifespan and have an important understanding of the anatomy and physiology involved with the airway from a dental perspective, it seems reasonable that better understanding and management from their field will give the opportunity to provide better integrated and optimized outcomes for children affected by OSA. With the emergence of therapies such as mandibular advancement devices and maxillary expansion, etc., dentists can be involved in providing care for OSA along with sleep medicine doctors. Furthermore, the evolving role of myofunctional therapy may also be indicated as adjunctive therapy in the management of children with OSA. The objective of this article is to discuss the important role of dentists and the collaborative approach between dentists, allied dental professionals such as myofunctional therapists, and sleep medicine specialists for identifying and managing children with OSA. Prevention and anticipatory guidance will also be addressed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".