ASSESSMENT BY ORAL HEALTH PROFESSIONALS OF PATIENT-REPORTED OUTCOMES IN OBSTRUCTIVE SLEEP APNEA
Bibliographic record
Abstract
Oral health professionals, including general dentists and orthodontists, may participate in the provision of care to adults and children with obstructive sleep apnea (OSA) when referred by a sleep medicine specialist. The use of oral appliances (OA) has been presented as a possible management option for mild obstructive sleep apnea among adults. In children, although oral interventions are not supported as a first-line stand-alone management option for OSA, short-term studies have suggested an improvement in sleep signs and symptoms in milder cases. The monitoring of management outcomes from OA interventions is essential to monitor OSA conditions. Due to the chronic nature of OSA, the measure of dental patient-reported outcomes (dPROs) will likely help to understand interplaying factors that may influence the effectiveness of OA interventions among subjects with OSA. This narrative review discusses the current evidence regarding main dPROs in OSA, evaluation methods, and the contribution of oral health professionals in assessing it for both children and adults. The main dPROs assessed among OA users include quality of life, treatment adherence, and side effects. The use of validated tools to measure dPROs must be preferred when available. Overall, the clinical assessment of dPROs by oral health professionals is likely beneficial to monitoring OSA management outcomes and must be encouraged as part of an interdisciplinary approach led by the sleep physician.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".