Long-Term Effects of Lung Volume Recruitment on Maximal Inspiratory Capacity and Vital Capacity in Duchenne Muscular Dystrophy
Bibliographic record
Abstract
RATIONALE: Lung volume recruitment therapy slows rate of decline of lung function in neuromuscular disease, possibly due to enhanced airway clearance, reduced atelectasis, or prevention of chest wall contractures. OBJECTIVES: To determine if lung volume recruitment maintains maximal insufflation capacity (MIC), despite decline in VC. METHODS: This was a retrospective cohort study (1991-2008) of individuals with Duchenne muscular dystrophy at pediatric and adult tertiary centers. Lung volume recruitment was prescribed twice daily, according to protocol. Changes over time in MIC, VC percentage predicted, the difference between MIC and VC, maximum inspiratory and expiratory pressures, and assisted and unassisted peak cough flow (PCF) were assessed using linear mixed effects models. MEASUREMENTS AND MAIN RESULTS: Sixteen individuals, 8.6 to 33.0 years old at initiation of lung volume recruitment, with median VC percentage predicted of 13.5 (interquartile range, 8.0-20.3), were followed over a median of 6.1 years (range, 1.7-16.1 yr). MIC-VC differences were stable (change, 0.02 L/yr; P = 0.06). Post-lung volume recruitment, compared with pretreatment, rate of decline in VC decreased from 4.5% predicted/yr to 0.5% predicted/yr (P < 0.001). Maximal inspiratory and expiratory pressures were unchanged (P = 0.08, 0.59 respectively). Assisted-spontaneous PCF difference was maintained (slope, -1.59 L/min/yr, P = 0.35). CONCLUSIONS: With lung volume recruitment therapy, MIC-VC differences were stable over time, indicating that respiratory system compliance remains stable, despite a loss in VC, in individuals with Duchenne muscular dystrophy. Decline in VC was significantly attenuated, and assisted PCF was maintained in a clinically effective range.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".