CPAP Treatment Adherence in Women with Obstructive Sleep Apnea
Bibliographic record
Abstract
Untreated obstructive sleep apnea (OSA) has numerous negative health-related consequences. Continuous positive airway pressure (CPAP) is generally considered the treatment of choice for OSA, but rates of nonadherence are high. It is believed that OSA is more prevalent among men; therefore understanding how OSA presents among women is limited and treatment adherence has received little research attention. For this study, 29 women were recruited from primary care offices. They completed a questionnaire battery and underwent a night of nocturnal polysomnography (PSG) followed by a visit with a sleep specialist. Women diagnosed with OSA were prescribed CPAP; 2 years later CPAP adherence was evaluated. Results show that approximately half the sample was adherent. There were no significant differences between adherent and nonadherent women on OSA severity; however CPAP adherent women had worse nocturnal and daytime functioning scores at the time of diagnosis. Moreover, when the seven nocturnal and daytime variables were used as predictors in a discriminant analysis, they could predict 87% of adherent and 93% of the nonadherent women. The single most important predictor was nonrefreshing sleep. We discuss the implications of the findings for identifying women in primary care with potential OSA and offer suggestions for enhancing treatment 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".