The Role of Biomarkers in Decreasing Risk of Cardiac Toxicity after Cancer Therapy
Bibliographic record
Abstract
With the improvement of cancer therapy, survival related to malignancy has improved, but the prevalence of long-term cardiotoxicity has also increased. Cancer therapies with known cardiac toxicity include anthracyclines, biologic agents (trastuzumab), and multikinase inhibitors (sunitinib). The most frequent presentation of cardiac toxicity is dilated cardiomyopathy associated with poorest prognosis. Monitoring of cardiac toxicity is commonly performed by assessment of left ventricular (LV) ejection fraction, which requires a significant amount of myocardial damage to allow detection of cardiac toxicity. Accordingly, this creates the impetus to search for more sensitive and reproducible biomarkers of cardiac toxicity after cancer therapy. Different biomarkers have been proposed to that end, the most studied ones included troponin release resulting from cardiomyocyte damage and natriuretic peptides reflecting elevation in LV filling pressure and wall stress. Increase in the levels of troponin and natriuretic peptides have been correlated with cumulative dose of anthracycline and the degree of LV dysfunction. Troponin is recognized as a highly efficient predictor of early and chronic cardiac toxicity, but there remains some debate regarding the clinical usefulness of the measurement of natriuretic peptides because of divergent results. Preliminary data are available for other biomarkers targeting inflammation, endothelial dysfunction, myocardial ischemia, and neuregulin-1. The purpose of this article is to review the available data to determine the role of biomarkers in decreasing the risk of cardiac toxicity after cancer therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".