Breast cancer treatment-induced cardiotoxicity
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is the most frequent cancer affecting women worldwide. In every setting, the majority of women are treated with an evergrowing arsenal of therapeutic agents that have greatly improved their outcomes. However, these therapies can also be associated with significant adverse events. Areas covered: This review aims to thoroughly describe the current state of the evidence regarding the potential cardiotoxicity of agents commonly used in the treatment of breast cancer. These include chemotherapeutic agents, anti-HER2 therapies and CDK4/6 and mTOR inhibitors. Furthermore, issues related to the risk stratification and monitoring tools are explored. Expert opinion: Anthracycline- and trastuzumab-related cardiac toxicities have been extensively studied. Substantial evidence is now available concerning additional anti-HER2 agents such as pertuzumab, T-DM1 and tyrosine kinase inhibitors; overall, the cardiotoxicity profile is reassuring. Cardiac events due to endocrine therapy are mostly ischemic and, in the context of prolonged therapy, need specific attention. Novel agents implicated in the treatment of hormone receptor-positive disease are potentially arrhythmogenic and the exact risk will need to be further refined. As for today, assessment of baseline risk factors prior to treatment initiation and cardiac imaging before and during treatment remains the optimal way to prevent cardiac dysfunction. Cardioprotective therapy in primary prevention is still a matter of debate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".