Evaluation, prevention and management of cancer therapy-induced cardiotoxicity
Bibliographic record
Abstract
PURPOSE OF REVIEW: While targeted therapies have improved cancer outcomes, unique cardiovascular toxicities are increasingly recognized, particularly when administered sequentially after anthracyclines or radiation. Patients with cancer therapy-induced cardiotoxicity benefit from collaborative care involving cardiology and oncology, leading to a new interdisciplinary field called cardio-oncology. The present review will highlight contemporary clinical issues in cardio-oncology. RECENT FINDINGS: Recently, risk factors for cancer therapy-induced cardiotoxicity have been evaluated in real-world rather than in clinical trial patients. Biomarkers and advanced echocardiography are emerging as sensitive tools for preclinical identification of cancer therapy-induced cardiotoxicity. Single-center studies suggest that cancer therapy-induced cardiotoxicity responds to prompt heart failure medical treatment, and such therapy may even prevent cardiotoxicity. SUMMARY: Modern cancer therapy has short-term cardiac risk that may require collaborative management by clinicians with expertise in cardiology and oncology. The increased effectiveness of modern cancer therapy is resulting in a growing population of cancer survivors who are at long-term risk for cardiovascular disease. The present review of contemporary clinical issues in cardio-oncology will be of interest to healthcare providers who manage cardiotoxicity during cancer therapy, and who follow patients who survive cancer but face increased long-term cardiovascular risk.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".