A Contemporary Review of the Effects of Exercise Training on Cardiac Structure and Function and Cardiovascular Risk Profile: Insights From Imaging
Bibliographic record
Abstract
Exercise is a commonly prescribed therapy for patients with established cardiovascular disease or those at high risk for de novo disease. Exercise-based, multidisciplinary programs have been associated with improved clinical outcomes post myocardial infarction and is now recommended for patients with cancer at elevated risk for cardiovascular complications. Imaging studies have documented numerous beneficial effects of exercise on cardiac structure and function, vascular function and more recently on the cardiovascular risk profile. In this contemporary review, we will discuss the effects of exercise training on imaging-derived cardiovascular outcomes. For cardiac imaging via echocardiography or magnetic resonance, we will review the effects of exercise on left ventricular function and remodeling in patients with established or at risk for cardiac disease (myocardial infarction, heart failure, cancer survivors), and the potential utility of exercise stress to assess cardiac reserve. Exercise training also has salient effects on vascular function and health including the attenuation of age-associated arterial stiffness and thickening as assessed by Doppler ultrasound. Finally, we will review recent data on the relationship between exercise training and regional adipose tissue deposition, an emerging marker of cardiovascular risk. Imaging provides comprehensive and accurate quantification of cardiac, vascular and cardiometabolic health, and may allow refinement of risk stratification in select patient populations. Future studies are needed to evaluate the clinical utility of novel imaging metrics following exercise training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".