Critical Issues in Dental and Medical Management of Obstructive Sleep Apnea
Bibliographic record
Abstract
This critical review focuses on obstructive sleep apnea (OSA) and its management from a dental medicine perspective. OSA is characterized by ≥10-s cessation of breathing (apnea) or reduction in airflow (hypopnea) ≥5 times per hour with a drop in oxygen and/or rise in carbon dioxide. It can be associated with sleepiness and fatigue, impaired mood and cognition, cardiometabolic complications, and risk for transportation and work accidents. Although sleep apnea is diagnosed by a sleep physician, its management is interdisciplinary. The dentist's role includes 1) screening patients for OSA risk factors (e.g., retrognathia, high arched palate, enlarged tonsils or tongue, enlarged tori, high Mallampati score, poor sleep, supine sleep position, obesity, hypertension, morning headache or orofacial pain, bruxism); 2) referring to an appropriate health professional as indicated; and 3) providing oral appliance therapy followed by regular dental and sleep medical follow-up. In addition to the device features and provider expertise, anatomic, behavioral, demographic, and neurophysiologic characteristics can influence oral appliance effectiveness in managing OSA. Therefore, OSA treatment should be tailored to each patient individually. This review highlights some of the putative action mechanisms related to oral appliance effectiveness and proposes future research directions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".