0985 Increasing Adherence To Mandibular Advancement Devices For Obstructive Sleep Apnea
Bibliographic record
Abstract
The prevalence of sleep apnea is estimated at approximately 26% and 13% of adult males and females, respectively. Long-term adherence to treatment for chronic conditions in developed countries is estimated at 50%. The aim of this study was to test whether a multifactorial intervention increases adherence rates in patients using a titratable oral appliance to treat OSA. Subjects are between the ages of 18-60+, and have a diagnosis of OSA. Subjects were randomly assigned to the experimental or control group (routine care). Experimental subjects received additional printed material and communication monthly. Comparison of adherence was at 1-, 3-, and 6 months. Variables were: mean nights adherent to prescribed wear time (≥4 hours, N≥4), and the mean hours worn per night (H/N). Compliance was measured by a micro-sensor (DentiTrac, Braebon, Ontario, CA) embedded in the oral appliance. Fifty-nine subjects have been enrolled to date, 25 females and 34 males. There were no significant differences between groups for age or sex. BMI was significantly higher, 5%, in the control group. For N ≥4 there was no statistical difference at 1 month between groups. At 3 and 6 months, the experimental group had worn their appliances 19 and 36 more nights at N≥4 (p< 0.007, p<0.001 respectively). For H/N, wear in the experimental group was 6.6h/n compared with 5.1h/n in the control group i.e.,1.5h/n longer (p< 0.0001) at 6 months. Interventions were well received by subjects, and most can be carried out by auxiliary personnel. The experimental interventions resulted in clinically significant improvements in patient adherence to treatment based on hours per night worn. This research was supported by a grant from the American Academy of Dental Sleep Medicine and the American Sleep Medicine Foundation.The DentiTrac micro-sensors were donated by Braebon Medical Corporation (Ontario, Canada)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".