Diaphragm Fatigue after Submaximal Exercise with Chest Wall Restriction
Bibliographic record
Abstract
PURPOSE: We asked whether submaximal exercise with chest wall restriction (CWR), as a model of restrictive pulmonary disease, would result in fatigue of the diaphragm in healthy humans. METHODS: To address this question, we used cervical magnetic stimulation of the phrenic nerves along with measures of transdiaphragmatic pressure. Seven healthy young men (30 ± 7 yr) completed pulmonary function tests and a maximal cycle exercise test. On a separate day, baseline measures of diaphragm contractility were obtained followed by cycle exercise at 45% of maximum intensity for 10 min with no restriction (NCWR). Diaphragm contractility was assessed again at 10 and 30 min after exercise. One hour later, inelastic straps were applied to reduce forced vital capacity by 40% followed by exercise at the same intensity. Diaphragm contractility was reassessed at 10 and 30 min after exercise. During exercise, the work of breathing, respiratory pressures, ventilatory parameters, and perceptions of respiratory and leg discomfort were recorded. RESULTS: The work of breathing and dyspnea ratings were greater during CWR exercise compared with NCWR (P < 0.05). The CWR condition had reductions in diaphragm contractility 10 min after exercise using nonpotentiated (-20.2% ± 15.3%) or potentiated twitches (-23.3% ± 12.4%, P < 0.05). There were no differences after exercise for NCWR (P > 0.05). The reduction in diaphragm contractility was correlated with the inspiratory elastic work of breathing (r2 = 0.74, P < 0.05). CONCLUSIONS: We conclude that fatigue of the diaphragm occurs under restricted conditions and likely contributes to poor exercise tolerance in patients with restrictive disease.
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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) | 0.001 | 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".