A Prospective Population-Based Study of Cardiovascular Disease Mortality following Treatment for Breast Cancer among Men in the United States, 2000–2019
Bibliographic record
Abstract
Male breast cancer is rare but its incidence and mortality are increasing in the United States, with racial/ethnic disparities in survival reported. There is limited evidence for cardiotoxicity of cancer treatment among men with breast cancer. We evaluated the relation between breast cancer treatment and cardiovascular disease (CVD) mortality among men and investigated the salient roles that race/ethnicity play on this relation. Data were from 5216 men with breast cancer aged ≥ 40 years from the Surveillance, Epidemiology, and End Results program who were diagnosed from 2000 to 2019 and underwent surgery. Competing risk models were used to estimate hazards ratios (HR) and 95% confidence intervals (CI). During a median follow-up of 5.6 years, 1914 deaths occurred with 25% attributable to CVD. In multivariable-adjusted models, men who received chemotherapy had elevated risk for CVD (HR: 1.55, 95%CI: 1.18-2.04). This risk was higher among Hispanic men (HR: 3.96, 95%CI: 1.31-12.02) than non-Hispanic Black and non-Hispanic White men. There was no significant association between radiotherapy and CVD deaths. In this population-based study, treatment with chemotherapy was associated with elevated risk of CVD mortality in men with breast cancer. Racial/ethnic disparities in the association of chemotherapy and CVD mortality were observed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".