Does dynamic hyperinflation contribute to dyspnoea during exercise in patients with COPD?
Bibliographic record
Abstract
Dynamic hyperinflation (DH) during exercise occurs in most but not all patients with advanced chronic obstructive pulmonary disease (COPD). It is not known whether the presence or absence of DH has implications for dyspnoea and exercise tolerance. Therefore, we compared detailed ventilatory and sensory responses to exercise in hyperinflators and nonhyperinflators with moderate-to-severe COPD. Nonhyperinflators (n=65) were retrospectively identified from a sample of 427 patients and case-matched to a group of hyperinflators (n=65) based on sex, age, body mass index and % predicted forced expiratory volume in 1 s. Resting pulmonary function and constant work rate cycle exercise responses were compared. Hyperinflators decreased inspiratory capacity (IC) from rest to peak exercise by 0.46±0.24 L whereas the nonhyperinflators increased IC by 0.10±0.15 L (p<0.0001). There were no significant group differences in endurance time (9.11±5.98 versus 8.87±5.24 min) or dyspnoea intensity for any given time or ventilation. An inflection in tidal volume versus ventilation occurred in the majority of nonhyperinflators (n=61) and hyperinflators (n=62) at a similar time and ventilation. Mechanical constraints on tidal volume expansion and the attendant rise in dyspnoea intensity were similar in both groups. Dyspnoea intensity during exercise was associated with progressive mechanical constraints on tidal volume expansion regardless of the presence of DH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".