Non‐invasive ventilation usage and adherence in children and adults with Duchenne muscular dystrophy: A multicenter analysis
Bibliographic record
Abstract
INTRODUCTION/AIMS: Non-invasive ventilation (NIV) is routinely prescribed to support the respiratory system in Duchenne muscular dystrophy (DMD) patients; however, factors improving NIV usage are unclear. We aimed to identify predictors of NIV adherence in DMD patients. METHODS: This was a multicenter retrospective analysis of DMD patients prescribed NIV and followed at (1) The Hospital for Sick Children, Canada; (2) Rady Children's Hospital San Diego, USA; and (3) University of California San Diego Health, USA, between February 2016 and October 2020. The primary and secondary outcomes were 90-day period NIV adherence and clinical and socioeconomic predictors of NIV adherence. RESULTS: We identified 59 DMD patients prescribed NIV (mean ± SD age = 20.1 ± 6.7 y). Overall, percentage of nights used, and average nightly usage, were 79.9 ± 31.1% and 7.23 ± 4.12 h, respectively. Compared with children, adults had higher percentage of nights used (92.9 ± 16.9% vs. 70.4 ± 36.9%; P < .05), and average nightly usage (9.5 ± 4.7 h vs. 5.3 ± 3.7 h; P < .05). Non-English language (P = .01), and absence of deflazacort prescription (P = .02) were significantly associated with higher percentage of nights used while Hispanic ethnicity (P = .01), low household income (P = .02), and absence of deflazacort prescription (P = .02) were significantly associated with higher nightly usage. Based on univariable analysis, older age and declining forced vital capacity were associated with increased percentage of nights used and increased average nightly usage. DISCUSSION: Certain clinical and socioeconomic determinants had a significant impact on NIV adherence in DMD patients, providing insight into those at risk for high versus low compliance with respiratory therapy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".