Central haemodynamics and peripheral muscle function during exercise in patients with chronic heart failure
Bibliographic record
Abstract
In this narrative review of the current literature, we examine the central and peripheral mechanisms responsible for the exercise intolerance of chronic heart failure and highlight briefly the benefits of exercise training in the treatment of this debilitating disorder. Specifically, we identify the common finding of reduced cardiac output reserve during exercise conditions leading to decreased exercise tolerance. We also reveal that the stroke volume response to exercise varies depending on the individual patient, the presence of mitral regurgitation, and the aetiology of heart failure. Chronic heart failure patients with left ventricular systolic dysfunction appear able to use the Frank-Starling mechanism to compensate (in part) for their decreased contractile reserve. Patients with left ventricular diastolic dysfunction have normal contractile function; however, they are unable to make use of the Frank-Starling mechanism during exercise conditions. We also reveal that pericardial constraint may limit diastolic filling and exercise capacity in patients with chronic heart failure. It appears that interventions that reduce pericardial constraint and mitral regurgitation enhance diastolic filling and increase exercise tolerance. A series of peripheral muscle changes also occur, including changes in muscle mass, cellular structure, energy metabolism, and blood flow. Each of these factors is associated with decreased exercise capacity and the symptoms of chronic heart failure. Exercise training has been shown to improve both central haemodynamics and peripheral muscle function leading to improvements in exercise capacity, functional status, and overall quality of life in patients with chronic heart failure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".