Long-Term Risk of Death From Heart Disease Among Breast Cancer Patients
Bibliographic record
Abstract
Background: Most breast cancer patients die of non-cancer causes. The risk of death from heart disease, a leading cause of death, is unknown. The aim of this study is to characterize the long-term risk of fatal heart disease in breast cancer patients. Methods: This retrospective study used the Surveillance, Epidemiology, and End Results (SEER) database. Standard mortality ratios (SMR) were calculated for breast cancer patients diagnosed from 1992 to 2014. Patients were stratified by receipt of radiotherapy and/or chemotherapy, disease laterality, and diagnosis era. Hazard ratios (HRs) and odds ratios (ORs) were calculated to compare the risk of death from heart disease among other breast cancer patients. Results: < 0.001). There was no statistically significant difference in SMR for death from heart disease for left-sided vs. right-sided disease. At 10+ years, heart disease made up 28% of deaths from non-primary cancer. HRs and ORs showed that the risk of death from heart disease was highest in patients older than 70 years of age and with longer follow-up. Conclusion: The risk of fatal heart disease was highest in older breast cancer patients with longer follow-up (i.e., >5-10 years) and who received chemotherapy. These patients should be referred to cardio-oncology clinics to mitigate this 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".