Proportional Assist Ventilation May Improve Exercise Performance in Severe Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
PURPOSE: Exercise tolerance is impaired in chronic obstructive pulmonary disease (COPD), in part because of a reduction in ventilatory capacity and excessive dyspnea experienced. The authors reasoned that proportional assist ventilation (PAV), a ventilator mode in which the level of support varies proportionately with patient effort, could be used during exercise to assist ventilation. The purpose of this study was to evaluate the efficacy of PAV to improve exercise endurance and related physiologic parameters in COPD. METHODS: In 8 patients (age = 62.8 years mean, +/- 6.9 standard deviation) with severe COPD (forced expiratory volume in 1 second = 0.70 +/- 0.21 L) flow, volume, dyspnea, leg fatigue, arterial blood gases, and gas exchange were measured during constant workrate exercise (37 +/- 18 watts; i.e., 80% previously determined maximum oxygen consumption). Crossover exercise trials were performed in random order: while spontaneously breathing through the experimental circuit without assistance (control trial) and with PAV (using 9.8 +/- 2.1 cm H2O/L and 3.3 +/- 1.0 cm H2O/L/sec of volume assist and flow assist, respectively). RESULTS: The application of PAV during exercise was well tolerated by each subject. Compared with the control measurement at equivalent time during exercise, PAV improved breathing pattern and arterial blood gases while dyspnea was reduced. Consequently, there was a significant increase in exercise duration with PAV (323 +/- 245 seconds during the control trial compared with 507 +/- 334 seconds with PAV, P = 0.02). CONCLUSIONS: Proportional assist ventilation can improve performance during constant workrate exercise in severe COPD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".