Patterns of Adherence to Lung Volume Recruitment Therapy Amongst Individuals With Duchenne Muscular Dystrophy: A Secondary Analysis of the STEADFAST Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Duchenne muscular dystrophy (DMD) is associated with impaired airway clearance and chest wall restriction, treated with lung volume recruitment (LVR) therapy. This study evaluated adherence patterns to LVR over 2 years in a cohort of boys with DMD. METHODS: Participants were included from the intervention arm of STEADFAST, a multi-centre randomized controlled trial of twice-daily LVR over 2 years in boys with DMD, 6-16 years with baseline forced vital capacity percent predicted (FVC%) ≥ 30% (Clinicaltrials. gov # NCT01999075). Adherence data were downloaded from a data logger on the LVR equipment and defined as LVR use at least daily on >50% of days. Logistic regression models evaluated associations between adherence and change in FVC%, rate of symptoms and change in quality of life (QOL) over 24 months. RESULTS: Fourteen of 33 boys (42.4%) were adherent to LVR. Among those non-adherent in the first 3 months, only one became adherent to LVR. Adherent participants tended to have lower baseline FVC % predicted than non-adherent individuals (median 77.2 (IQR (17.8) vs 89.7 (18.8) %; p = 0.08). The odds ratio for adherence was 1.02 (95% CI 0.97-1.08, p = 0.4) per unit increase in FVC%, and 0.08 (95% CI 0.001-11.7, p = 0.307) for each additional symptom/month over 24 months, adjusting for baseline age and ambulatory status. Adherence was not associated with QOL. CONCLUSIONS: Long-term LVR adherence was associated with early LVR usage pattern, but not change in lung function, symptoms or QOL. These findings underscore the importance of early support and education to promote long-term LVR 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".