Mechanisms of Exertional Dyspnea in Patients with Mild COPD and a Low Resting DL<sub>CO</sub>
Bibliographic record
Abstract
Patients with mild chronic obstructive pulmonary disease (COPD) and lower resting diffusing capacity for carbon monoxide (DLCO) often report troublesome dyspnea during exercise although the mechanisms are not clear. We postulated that in such individuals, exertional dyspnea is linked to relatively high inspiratory neural drive (IND) due, in part, to the effects of reduced ventilatory efficiency. This cross-sectional study included 28 patients with GOLD I COPD stratified into two groups with (n = 15) and without (n = 13) DLCO less than the lower limit of normal (<LLN; Global Lung Function Initiative criteria) and 16 healthy controls. We compared dyspnea (Borg scale), IND (by diaphragm electromyography), ventilatory equivalent for CO2 (V̇E/V̇CO2), and respiratory mechanics during incremental cycle exercise in the three groups. Spirometry and resting lung volumes were similar between COPD groups. During exercise, dyspnea, IND and V̇E/V̇CO2 were higher at equivalent work rates (WR) in the DLCO<LLN group compared with the other two groups (all p < 0.05). In patients with DLCO<LLN, severe respiratory mechanical constraints, indicated by end-inspiratory lung volume of approximately 90% of total lung capacity, occurred at a lower WR than the other two groups (p < 0.05). The dyspnea/IND relationship was similar across groups; therefore, the increased dyspnea at a standardized WR in the low DLCO<LLN group reflected the higher corresponding IND. Higher dyspnea ratings in patients with mild COPD and DLCO<LLN were associated with higher IND and V̇E/V̇CO2 at a given work rate. Higher ventilatory requirements in the DLCO<LLN group accelerated dynamic mechanical abnormalities earlier in exercise, further increasing IND and dyspnea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".