An emerging epidemic: cancer and heart failure
Bibliographic record
Abstract
Heart disease and cancer are the two leading causes of mortality globally. Cardiovascular complications of cancer therapy significantly contribute to the global burden of cardiovascular disease. Heart failure (HF) in particular is a relatively common and life-threatening complication. The increased risk is driven by the shared risk factors for cancer and HF, the direct impact of cancer therapy on the heart, an existing care gap in the cardiac care of patients with cancer and the increasing population of adult cancer survivors. The clear relationship between cancer treatment initiation and the potential for myocardial injury makes this population attractive for prevention strategies, targeted cardiovascular monitoring and treatment. However, there is currently no consensus on the optimal strategy for managing this at-risk population. Uniform treatment using cardioprotective medications may reduce the incidence of HF, but would impose frequently unnecessary and burdensome side effects. Ideally we could use validated risk-prediction models to target HF-preventive strategies, but currently no such models exist. In the present review, we focus on evidence and rationales for contemporary clinical decision-making in this novel field and discuss issues, including the burden of HF in patients with cancer, the reasons for the elevated risk and potential prevention strategies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".