To Wear or Not Wear the Mask: Decline in Positive Airway Pressure Usage in Children with Sleep Disordered Breathing During the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: Positive airway pressure (PAP) therapy is an effective treatment prescribed to children with sleep disordered breathing (SDB); however, PAP adherence remains challenging. Given that COVID-19 pandemic continues to impact sleep and daily life, the aim of this study was to evaluate longitudinal trajectory of PAP usage in children during the COVID-19 pandemic. Patients and Methods: This was a retrospective study. Children aged 1-18 years with SDB prescribed PAP at The Hospital for Sick Children (Toronto, Canada) were evaluated for PAP adherence. Demographics, medical history and PAP adherence data during four consecutive 3-month time periods from December 2019 to December 2020 were collected. These four time periods included i) prior to COVID-19 lockdown, ii) during the first three months of lockdown, iii) summer and iv) return to school period. Percentage of days where PAP was used for ≥4 hours and average nightly usage of PAP were primary outcomes. Results: A total of 149 children (61.7% male, mean (±SD) age=12.8 ± 4.1 years, BMI (±SD) z-score=1.45±1.43) were enrolled. Compared to prior to lockdown, the median (IQR) of percentage of PAP usage ≥4 hours and average nightly usage of PAP declined significantly during the summer and return to school periods (p<0.001 for all). By the end of the return to school period, only 69/149 (46%) showed sustained PAP usage and 80/149 (54%) had decreased PAP usage. Obesity was a risk factor for a decline in PAP usage after returning to school (β=-15.36, p=0.03). Conclusion: Compared to COVID-19 pre-pandemic PAP usage, there was a significant decline in PAP usage across COVID-19 pandemic. There is critical under usage of PAP in children diagnosed with SDB, resulting in an urgent need to address barriers to mitigate poor adherence to PAP long-term. Targeted strategies are required to optimize PAP adherence in children with SDB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".