Mechanical cardiopulmonary interactions during exercise in health and disease
Bibliographic record
Abstract
The heart and lungs are anatomically coupled through the pulmonary circulation and coexist within the sealed thoracic cavity, making the function of these systems highly interdependent. Understanding of the complex mechanical interactions between cardiac and pulmonary systems has evolved over the last century to appreciate that changes in respiratory mechanics significantly impact pulmonary hemodynamics and ventricular filling and ejection. Furthermore, given that the left and right heart share a common septum and are surrounded by the nondistensible pericardium, direct ventricular interaction is an important mediator of both diastolic and systolic performance. Although it is generally considered that cardiopulmonary interaction in healthy individuals at rest minimally affects hemodynamics, the significance during exercise is less clear. Adverse heart-lung interaction in respiratory disease is of growing interest as it may contribute to the pathogenesis of comorbid cardiovascular dysfunction and exercise intolerance in these patients. Similarly, heart failure represents a pathological uncoupling of the cardiovascular and pulmonary systems, whereby cardiac function may be impaired by the normal ventilatory response to exercise. Despite significant research contributions to this complex area, the mechanisms of cardiopulmonary interaction in the intact human and the clinical consequences of adverse interactions in common respiratory and cardiovascular diseases, particularly during exercise, remain incompletely understood. The purpose of this review is to present the key physiological principles of cardiopulmonary interaction as they pertain to resting and exercising hemodynamics in healthy humans and the clinical implications of adverse cardiopulmonary interaction during exercise in chronic obstructive pulmonary disease (COPD), pulmonary hypertension, and 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".