Preventing broken hearts in women with breast cancer: a concise review on chemotherapy-mediated cardiotoxicity
Bibliographic record
Abstract
Cancer and cardiovascular disease are the leading causes of death for Canadian women. One in eight Canadian women will receive the life-changing diagnosis of breast cancer (BC) in their lifetime, with 1 in 34 dying from the disease. Although doxorubicin (DOX) and trastuzumab (TRZ) have significantly improved survival in women diagnosed with human epidermal growth factor receptor 2 (HER2)-positive BC, approximately one in four women who receive this treatment are at risk of developing chemotherapy-induced cardiotoxicity. Cardiotoxicity is defined as a decline in left ventricular ejection fraction (LVEF) of >10% to an absolute value of <53%. Current guidelines recommend the serial monitoring of LVEF in this patient population using non-invasive cardiac imaging modalities including transthoracic echocardiography or multi-gated acquisition scan; however, this will only allow for the detection of established cardiotoxicity. Recent studies have demonstrated that a reduction in global longitudinal strain by speckle tracking echocardiography can identify pre-clinical systolic dysfunction prior to a decline in overall LVEF. Implementation of early detection techniques would allow for the prompt initiation of cardioprotective strategies. In addition to the early detection of chemotherapy-mediated cardiotoxicity, the prophylactic use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, β-blockers, statins, exercise, and nutraceutical therapies have been studied in the setting of cardio-oncology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".