Personalized Care in the Prevention of Treatment-Related Cardiac Dysfunction in Female Cancer Survivors
Bibliographic record
Abstract
Background: The American Cancer Society projects the number of U.S. cancer survivors to exceed 20 million individuals by 2026. However, approximately one in four cancer survivors report decreased quality of life due to physical dysfunction and disabling symptoms. Many effective anticancer treatments are now understood to be associated with cardiotoxicity, such that, for many survivors, the risk of death from cardiovascular disease now exceeds that of recurrent cancer. Materials and Methods: We undertook a Clinical Review of cancer treatment-related cardiac dysfunction (CTRCD) associated with standard treatment regimens with attention to risks experienced by female cancer patients and survivors. Results: Risks of standard (chemotherapy, radiotherapy) and targeted (antibodies, kinase inhibitors) in development of CTCRD in females are discussed. Multidisciplinary approaches in prevention are reviewed. Conclusions: Female cancer survivors with CTRCD represent an entirely new population at high risk of morbidity and mortality. Increased awareness of the short- and long-term effects of anti-cancer treatments is necessary for the community health care provider for early detection and CTRCD risk reduction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".