The Impact of Chemotherapy on Cardiovascular Mortality across Breast Cancer Subtypes
Bibliographic record
Abstract
Breast cancer is associated with cardiovascular mortality as an adverse effect of chemotherapy. Considering the variances across breast cancer subtypes, this study aims to investigate the cardiovascular mortality patterns in each subtype. Methods: This retrospective study used the SEER database of chemotherapy-receiving breast cancer patients (diagnosed in 2013–2020). The study population was categorized by cancer subtype, stage, patient age, and cause of death (COD). The percentage of cardiovascular CODs, odds ratio (ORs), 5-year cumulative crude probability of death, and standardized mortality ratios (SMRs) of each group were analyzed. Results: Among 23,263 nonsurviving breast cancer patients, 5.8% died from cardiovascular disease, whereas the HER2+/HR+ and HER2+/HR− subtypes exhibited the highest ORs of cardiovascular death and percentages of cardiovascular CODs, at 8.21% and 6.55%, respectively. The cardiovascular SMR increased with advancing stages and decreasing patient age. The HER2+/HR- subtype had the highest cardiovascular SMR, at 0.83 (p < 0.05), followed by TNBC, at 0.78 (p < 0.05). The 5-year cumulative probability of cardiovascular CODs also showed the highest risk in the HER2+/HR- subtype (1.02 ± 0.11%) and the TNBC subtype (0.95 ± 0.07%). Conclusion: Breast cancer patients on chemotherapy face an elevated cardiovascular mortality risk, especially with aggressive subtypes (HER2-enriched, TNBC), advanced age, or HER2+/HR+ cancer receiving long-term treatment.
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.
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 | 0.003 |
| 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 | 0.000 |
| 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".