Lung Volume Recruitment in Multiple Sclerosis
Bibliographic record
Abstract
INTRODUCTION: Pulmonary function abnormalities have been described in multiple sclerosis including reductions in forced vital capacity (FVC) and cough but the time course of this impairment is unknown. Peak cough flow (PCF) is an important parameter for patients with respiratory muscle weakness and a reduced PCF has a direct impact on airway clearance and may therefore increase the risk of respiratory tract infections. Lung volume recruitment is a technique that improves PCF by inflating the lungs to their maximal insufflation capacity. OBJECTIVES: Our goals were to describe the rate of decline of pulmonary function and PCF in patients with multiple sclerosis and describe the use of lung volume recruitment in this population. METHODS: We reviewed all patients with multiple sclerosis referred to a respiratory neuromuscular rehabilitation clinic from February 1999 until December 2010. Lung volume recruitment was attempted in patients with FVC <80% predicted. Regular twice daily lung volume recruitment was prescribed if it resulted in a significant improvement in the laboratory. RESULTS: There were 79 patients included, 35 of whom were seen more than once. A baseline FVC <80% predicted was present in 82% of patients and 80% of patients had a PCF insufficient for airway clearance. There was a significant decline in FVC (122.6 mL/y, 95% CI 54.9-190.3) and PCF (192 mL/s/y, 95% 72-311) over a median follow-up time of 13.4 months. Lung volume recruitment was associated with a slower decline in FVC (p<0.0001) and PCF (p = 0.042). CONCLUSION: Pulmonary function and cough decline significantly over time in selected patients with multiple sclerosis and lung volume recruitment is associated with a slower rate of decline in lung function and peak cough flow. Given design limitations, additional studies are needed to assess the role of lung volume recruitment in patients with multiple sclerosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".