Exercise Prevention of Cardiovascular Disease in Breast Cancer Survivors
Bibliographic record
Abstract
Thanks to increasingly effective treatment, breast cancer mortality rates have significantly declined over the past few decades. Following the increase in life expectancy of women diagnosed with breast cancer, it has been recognized that these women are at an elevated risk for cardiovascular disease due in part to the cardiotoxic side effects of treatment. This paper reviews evidence for the role of exercise in prevention of cardiovascular toxicity associated with chemotherapy used in breast cancer, and in modifying cardiovascular risk factors in breast cancer survivors. There is growing evidence indicating that the primary mechanism for this protective effect appears to be improved antioxidant capacity in the heart and vasculature and subsequent reduction of treatment-related oxidative stress in these structures. Further clinical research is needed to determine whether exercise is a feasible and effective nonpharmacological treatment to reduce cardiovascular morbidity and mortality in breast cancer survivors, to identify the cancer therapies for which it is effective, and to determine the optimal exercise dose. Safe and noninvasive measures that are sensitive to changes in cardiovascular function are required to answer these questions in patient populations. Cardiac strain, endothelial function, and cardiac biomarkers are suggested outcome measures for clinical research in this field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".