Diaphragm Activation during Exercise in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Although it has been postulated that central inhibition of respiratory drive may prevent development of diaphragm fatigue in patients with chronic obstructive pulmonary disease (COPD) during exercise, this premise has not been validated. We evaluated diaphragm electrical activation (EAdi) relative to maximum in 10 patients with moderately severe COPD at rest and during incremental exhaustive bicycle exercise. Flow was measured with a pneumotachograph and volume by integration of flow. EAdi and transdiaphragmatic pressures (Pdi) were measured using an esophageal catheter. End-expiratory lung volume (EELV) was assessed by inspiratory capacity (IC) maneuvers, and maximal voluntary EAdi was obtained during these maneuvers. Minute ventilation (V E) was 12.2 +/- 1.9 L/min (mean +/- SD) at rest, and increased progressively (p < 0.001) to 31.0 +/- 7.8 L/min at end-exercise. EELV increased during exercise (p < 0.001) causing end-inspiratory lung volume to attain 97 +/- 3% of TLC at end-exercise. Pdi at rest was 9.4 +/- 3.2 cm H(2)O and increased during the first two thirds of exercise (p < 0.001) to plateau at about 13 cm H(2)O. EAdi was 24 +/- 6% of voluntary maximal at rest and increased progressively during exercise (p < 0.001) to reach 81 +/- 7% at end-exercise. In conclusion, dynamic hyperinflation during exhaustive exercise in patients with COPD reduces diaphragm pressure-generating capacity, promoting high levels of diaphragm activation.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".