Inspiratory muscle training reduces respiratory neural drive (RND) during exercise in patients with COPD
Bibliographic record
Abstract
Mechanisms for improvements in dyspnea after inspiratory muscle training (IMT) have not been systematically studied. We investigated whether this might be ascribed to reductions in RND. Ten clinically stable patients with COPD (FEV 1 :61±20%pred.) with activity related dyspnea (Baseline Dyspnea Index:6.0±1.3) and inspiratory muscle weakness (Pi,max: 61±12cmH 2 O) were randomized into an intervention group (n=7), or a control group (n=3). Before and after an 8-week daily IMT program, an endurance cycling test was performed at 75% peak work rate. Patients rated dyspnea during exercise an a Borg CR-10 scale and diaphragm electromyography (EMGdi) was assessed with a multipair esophageal electrode catheter (Luo, Y.M. et al. Clin Sci 2008;115:233-44). IMT intensity either increased from 68±11% to 123±21% baseline Pi,max (intervention) or remained constant at 8±2% baseline Pi,max (control). Larger increases in Pi,max (28±13 vs 10±3cmH 2 O; p=0.009) and reductions in RND (-12±6% vs 3±11% EMGdimax; p=0.023; Figure) at standardized ventilation were observed in the intervention group. This was associated with larger improvements in endurance time (356±265sec vs 17±38sec; p=0.014; Figure) and dyspnea at standardized ventilation (-3.3±2.6 vs -0.3±0.6 Borg units; p=0.023). IMT decreases RND during exercise and this might be mechanistically related to improvements in dyspnea and exercise tolerance in selected patients with 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".