Exertional dyspnea and operating lung volumes in asthma
Bibliographic record
Abstract
Dyspnea has been reported to be a main contributor to exercise avoidance in asthma. While traditional markers of ventilation do not explain the heightened dyspnea during exercise in patients with asthma, this study proposed that exertional dyspnea in asthma was due to high-operating lung volumes, which may be improved with a short-acting β2-agonist. On two separate days, 16 patients with asthma and 16 controls completed a lung function test and incremental exercise tests to exhaustion. On one of the days (order randomized), 400 µg salbutamol was administered before exercise. Inspiratory capacity (IC), inspiratory reserve volume (IRV), and dyspnea (modified Borg scale) were evaluated throughout exercise. Compared with controls, patients with asthma reported greater dyspnea at the same absolute submaximal workloads. Furthermore, patients with asthma demonstrated altered breathing responses to exercise, characterized by reduced IC and IRV throughout exercise compared with controls. The reduced IRV was associated with increased dyspnea in patients with asthma. Salbutamol did not affect dyspnea or operating lung volumes in either group. The increased perception of dyspnea during incremental exercise in patients with asthma appears to be secondary to a reduction in IRV, which is unaffected by an inhaled β2-agonist. NEW & NOTEWORTHY Increased exertional dyspnea in asthma appears to be due to high operating lung volumes and is not affected by salbutamol.
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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".