Incident Myocardial Infarction, Heart Failure, and Oncologic Outcomes in Breast Cancer Survivors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it