Sleep Quality with EPAP Enhanced Novel Mandibular Advancement Device (EMAD) versus Continuous Positive Airway Pressure (CPAP) in Mild and Moderate Obstructive Sleep Apnea (OSA)
Bibliographic record
Abstract
Rationale: To compare self-reported sleep quality, treatment compliance, and respiratory event index (REI) after 1 year of treatment with EPAP enhanced novel MAD - O2Vent Optima+ExVent (EMAD) or CPAP in mild and moderate OSA. Materials and Methods: 46 mild and moderate OSA patients were treated with either EMAD or CPAP treatment and followed for 1 year. Data were collected through Level 1 PSG recordings, CPAP downloads, Epworth Sleepiness Scale (ESS), Stanford Sleepiness Scale (SSS) and Functional Outcomes of Sleep Questionnaire (FOSQ-10). We compared compliance, ESS, SSS, FOSQ-10 scores and REI between two the treatments. Reliable change index (RCI) was used to evaluate change in FOSQ global score. Results: Compliance with EMAD treatment (89.5%) was better than CPAP treatment (48.9%) at follow-up (P<0.001). Both groups had improved ESS, SSS and FOSQ-10 scores: EMAD (ESS:12.8.0±3.6 to 7.9±2.6; SSS: 3.4±1.2 to 2.2±0.7 FOSQ-10: 13.2±2.9 to 17.3±3.1; mean difference -2.9, (95% CI -3.5 to -2.4); P<0.001) and CPAP (ESS: 13.2±3.5 to 6.7±3.4, SSS: 3.3±1.1 to 2.2±0.9; FOSQ-10: 13.9±2.5 to 17.8±3.61; mean difference -3.1 (95% CI -3.2 to -2.4); P<0.001). More patients had improved FOSQ global score on the RCI in the EMAD group (43.6%) than in the CPAP group (16.7%) (P=0.01). Both treatments reduced REI to <10/hr. similarly; EMAD (84%) and CPAP (93%) (P=0.08). Conclusions: EMAD treatment was associated with better compliance and improved sleep quality than CPAP although REI was similarly reduced with both. We conclude that EMAD treatment should be considered a preferred treatment option than CPAP for mild and moderate OSA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".