Caregiver support and positive airway pressure therapy adherence among adolescents with obstructive sleep apnea
Bibliographic record
Abstract
INTRODUCTION: Positive airway therapy (PAP) adherence rates are suboptimal among adolescents with obstructive sleep apnea (OSA) and strategies to increase PAP adherence is a clinical priority. This study evaluates if caregiver support is associated with PAP adherence rates among adolescents with OSA. METHODS: We conducted a retrospective study and evaluated PAP adherence rates among adolescents with OSA from 2012 to 2017. Adherence was measured as continuous variables: average PAP usage (minutes per night) and average PAP usage >4 hours/night (% of all nights). We evaluated if adolescents with OSA who were receiving practical caregiver support with PAP had higher adherence than adolescents with OSA without caregiver support. RESULTS: One hundred and seven adolescents with OSA (mean age=14.1±2.5 years, 64.5% male, mean BMI percentile=89.0±21.8) seen between January 2012 and August 2017 at our institution were included. In this study, 60.7% (n=65) of adolescents with OSA were receiving practical caregiver support with PAP therapy. Adolescents with OSA receiving practical caregiver support with PAP used therapy for a significantly greater duration each night compared to adolescents who were not receiving practical caregiver support (298.5±206.7 versus 211.9±187.2 minutes; P=0.02). Greater time since the initial PAP prescription was independently associated with PAP adherence. CONCLUSION: Focusing on PAP adherence early may help adolescents with OSA incorporate therapy into their nightly routine, which may improve adherence and lead to improved health outcomes in adolescents with OSA. Practical caregiver support may be an essential component of ensuring optimal PAP adherence among adolescents with 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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".