Exertional dyspnoea in chronic heart failure: the role of the lung and respiratory mechanical factors
Bibliographic record
Abstract
Exertional dyspnoea is among the dominant symptoms in patients with chronic heart failure and progresses relentlessly as the disease advances, leading to reduced ability to function and engage in activities of daily living. Effective management of this disabling symptom awaits a better understanding of its underlying physiology.Cardiovascular factors are believed to play a major role in dyspnoea in heart failure patients. However, despite pharmacological interventions, such as vasodilators or inotropes that improve central haemodynamics, patients with heart failure still complain of exertional dyspnoea. Clearly, dyspnoea is not determined by cardiac factors alone, but likely depends on complex, integrated cardio-pulmonary interactions.A growing body of evidence suggests that excessively increased ventilatory demand and abnormal "restrictive" constraints on tidal volume expansion with development of critical mechanical limitation of ventilation, contribute to exertional dyspnoea in heart failure. This article will offer new insights into the pathophysiological mechanisms of exertional dyspnoea in patients with chronic heart failure by exploring the potential role of the various constituents of the physiological response to exercise and particularly the role of abnormal ventilatory and respiratory mechanics responses to exercise in the perception of dyspnoea in patients with heart failure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".