Challenges and opportunities for improving cardiovascular health in women with breast cancer: a review
Bibliographic record
Abstract
BACKGROUND: Breast cancer survivors are at a high risk of developing cardiovascular disease (CVD) owing to cancer treatment. Breast cancer and CVD share common risk factors, necessitating CVD risk assessment along with cancer screening. This review aimed to explore the challenges and opportunities associated with promoting cardiovascular health in women with breast cancer. MAIN TEXT: Cardio-oncology is a rapidly developing discipline that focuses on identifying, monitoring, and managing CVD in cancer patients. Preventing and managing CVD in patients with breast cancer involves evaluating risk factors, initiating cardioprotective medications, and implementing cardio-oncology rehabilitation. Major barriers to cardio-oncology prevention and management include inadequate programs, sex/gender-specific issues, financial constraints, underutilization of cardiac rehabilitation (CR), determination of the appropriate time to begin CR, physical limitations, psychological issues, and social and racial disparities. CONCLUSION: A preventive cardio-oncology approach; early identification of cardiotoxicity, CVD risk factors, anxiety, and depression; individualized CR programs; early CR referrals; home/community and virtual CR models; dedicated funding, resources, and personnel; a multidisciplinary team approach; and culturally tailored cardio-oncology care can be beneficial for addressing CVD health challenges and disparities in women with breast cancer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".