Temporal changes in treatments and outcomes after acute myocardial infarction among cancer survivors and patients without cancer, 1995 to 2013
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information about treatment and mortality trends after acute myocardial infarction (AMI) for cancer survivors (CS). METHODS: In this population-based study, the authors compared temporal trends of treatments and outcomes (mortality, nonfatal cardiovascular outcomes), among CS and patients without cancer (the noncancer patient [NCP] group) with AMI in Ontario (Canada) using inverse probability treatment weight (IPTW)-adjusted modeling. RESULTS: < .001). At 30 days after AMI, there was no difference between CS and NCP in the receipt of coronary angiography (incidence risk ratio [IRR], 0.98; 95% confidence interval [CI], 0.96-1.01; P = .23), percutaneous coronary intervention (IRR, 0.98; 95% CI, 0.94-1.02; P = .29), or bypass (IRR, 0.93; 95% CI, 0.85-1.02; P = .11). At 90 days after AMI, there was no difference in the receipt of β-blockers, clopidogrel, or nitrates; but CS were less often prescribed angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers and statins. CS had higher all-cause mortality at 30 days (adjusted hazard ratio [HR] 1.12; 95% CI, 1.07-1.17; P < .001), at 1 year (1.16; 95% CI, 1.12-1.20; P < .001), and long term (HR, 1.21; 95% CI, 1.17-1.25; P < .001) and had a greater risk of heart failure (HR, 1.08; 95% CI, 1.03-1.14; P = .001), but not myocardial re-infarction (HR, 0.98; 95% CI, 0.95-1.01; P = .22) or stroke (HR, 1.06; 95% CI, 0.97-1.16; P = .18). CONCLUSIONS: Among CS and NCP with AMI in Ontario, similar improvements in mortality and receipt of treatments were observed between 1995 and 2013. However, compared with NCP, CS had a higher risk of mortality and heart failure. Cancer 2018;124:1269-78. © 2017 American Cancer Society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".