Late Breaking Abstract - Enhanced PAP adherence using cMAP® for sleep apnea therapy vs. APAP: A preliminary 3-month analysis
Bibliographic record
Abstract
Introduction: Historically, adherence to positive airway pressure (PAP) therapy for obstructive sleep apnea (OSA) has been challenging. We are conducting a study comparing PAP therapy adherence and sleep-related outcomes between automatic-PAP (APAP) and continuous management of airway pressure (cMAP®), which leverages artificial intelligence for predicting and preventing OSA events. In a preliminary analysis, we assessed: 3-month differences in adherence, sleep-related outcomes, and key reasons for PAP discontinuation (DC). Methods: This is a double-blind, block randomized trial with a standardized coaching protocol. Seventy patients (cMAP®: 36, APAP: 34) reached 3-month follow-up (1-year target: N=200). Three-month US CMS compliance was assessed via Fisher Exact test; device usage and total clinician coaching time by unpaired t-test. A questionnaire identified DC reasons. Results: No baseline clinical or demographic differences were noted between arms. cMAP® patients showed significant improvements in 3-month adherence compared to APAP (93.9% [34/36] vs. 76.5% [26/34], p=0.04). Device usage significantly increased (5.3±1.5 vs. 4.6±2.5 hrs, p=0.014) while total coaching time decreased (19±12 vs. 36±24 mins, p<0.001) compared to APAP. The most prominent DC reason, “CPAP wakes me up too often when sleeping,” was exclusively cited by patients in the APAP arm (0 vs. 9). Conclusion: Improved adherence, device usage, and reduced coaching time on the cMAP® arm are promising. The absence of nocturnal arousal-related complaints leading to DC in the cMAP® arm may be due to the higher sleep quality index (SleepImageTM Ring) associated with cMAP® versus APAP (Hanafi. et al. ERJ 2024 64:PA4470).
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".