Objective adherence to oral appliance therapy in patients with obstructive sleep apnea: a one-year longitudinal analysis
Bibliographic record
Abstract
INTRODUCTION: Oral appliance (OA) therapy is widely used as an alternative to continuous positive airway pressure (CPAP) therapy for treating obstructive sleep apnea (OSA). Traditionally, OA adherence has been assessed through subjective self-reports before, but the availability of objective adherence sensors now allows for more accurate monitoring. This study aimed to analyze one-year objective adherence data to identify adherence patterns over time and factors influencing adherence to OA therapy. MATERIALS AND METHODS: Fifty-five OSA patients were recruited from a cohort study and underwent clinical follow-ups at baseline, 1, 6 and 12 months. Patients were treated with custom-made, titratable OAs, and adherence was objectively collected using embedded sensors. Adherence data were analyzed using both intention-to-treat (ITT) and per-protocol (PP) approaches. Statistical methods, including comparative analyses, logistic regression models, and multivariate linear regression were performed to identify predictors of adherence. RESULTS: Twenty-one patients dropped out before the 12-month follow-up, leaving 34 completed the entire study. At the 1-month follow-up, 80.0% of patients were classified as adherent, with a mean wearing time of 5.98 ± 2.38 hours per night. By 6 months, adherence decreased to 67.3%, with a mean wearing time of 5.69 ± 2.08 hours per night. Several significant predictors of adherence were identified, including larger baseline overjet, younger age, and marital status. CONCLUSIONS: OA adherence declined significantly within the first 6 months but stabilized between 6 and 12 months. Key baseline factors, such as larger overjet, younger age, and being married or partnered are predictors of better adherence, while psychological Comorbidities are associated with lower adherence.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".