Trends in CPAP adherence over twenty years of data collection: A flattened curve
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a common disorder, and continuous airway positive pressure (CPAP) is considered to be the gold standard of therapy. CPAP however is known to have problems with adherence, with many patients eventually abandoning the device. The purpose of this paper is to assess secular trends in CPAP adherence over the long term to see if there have been meaningful improvements in adherence in light of the multiple interventions proposed to do so. METHODS: A comprehensive systematic literature review was conducted using the Medline-Ovid, Embase, and Pubmed databases, searching for data regarding CPAP adherence over a twenty year timeframe (1994-2015). Data was assessed for quality and then extracted. The main outcome measure was reported CPAP non-adherence. Secondary outcomes included changes in CPAP non-adherence when comparing short versus long-term, and changes in terms of behavioral counseling. RESULTS: Eighty-two papers met study inclusion/exclusion criteria. The overall CPAP non-adherence rate based on a 7-h/night sleep time that was reported in studies conducted over the twenty year time frame was 34.1 %. There was no significant improvement over the time frame. Behavioral intervention improved adherence rates by ~1 h per night on average. CONCLUSIONS: The rate of CPAP adherence remains persistently low over twenty years worth of reported data. No clinically significant improvement in CPAP adherence was seen even in recent years despite efforts toward behavioral intervention and patient coaching. This low rate of adherence is problematic, and calls into question the concept of CPAP as gold-standard of therapy for 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 | 0.100 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.014 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".