Incident Myocardial Infarction, Heart Failure, and Oncologic Outcomes in Breast Cancer Survivors
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is associated with higher rates of incident cancer. Data are scarce regarding the association of incident CVD with oncologic outcomes after a cancer diagnosis. Objectives: This study sought to determine whether incident myocardial infarction (MI) or heart failure (HF) in breast cancer survivors is associated with oncologic outcomes. Methods: This was a population-based cohort study in Ontario, Canada, using linked administrative data sets of women diagnosed with first breast cancer between April 1, 2007, and March 31, 2015. A landmark analysis was conducted of women alive 2 years after breast cancer diagnosis, aged ≥40 years, and with available staging data and without recurrent/distant disease or preceding CVD. The exposure was a composite of MI and/or HF after the landmark date. The outcomes were cancer mortality, new non-breast malignancy diagnosis, and new chemotherapy initiation. Multivariable cause-specific hazards regression was used to determine the association of incident MI/HF (time-varying exposure) with outcomes. Results: A total of 30,694 women (median age of 60 years) were included, of whom 1,346 developed incident MI/HF at a median of 3.9 years after the landmark date. At 5 years, the cumulative incidence was 5.9% (95% CI: 5.6%-6.1%) for cancer death, 4.3% (95% CI: 4.1%-4.6%) for non-breast malignancy, and 25.7% (95% CI: 25.2%-26.2%) for new chemotherapy. Incident MI/HF was associated with a higher hazard of cancer death (HR: 3.94; 95% CI: 3.38-4.59), non-breast malignancy (HR: 1.39; 95% CI: 1.06-1.82), and new chemotherapy (HR: 1.25; 95% CI: 1.02-1.53). Conclusions: Incident MI and/or HF after breast cancer treatment are associated with higher hazards of adverse oncologic outcomes, highlighting the need to prioritize care for these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".