Coronary artery disease in patients with cancer: challenges and opportunities for improvement
Bibliographic record
Abstract
PURPOSE OF REVIEW: Coronary artery disease (CAD) is a common comorbidity in patients with cancer. We review shared risk factors between the two diseases and cancer treatments that increase the risk of CAD. We also discuss outcomes and management considerations of patients with cancer who develop CAD. RECENT FINDINGS: Several traditional and novel risk factors promote the development of both CAD and cancer. Several cancer treatments further increase the risk of CAD. The presence of cancer is associated with a higher burden of comorbidities and thrombocytopenia, which predisposes patients to higher bleeding risks. Patients with cancer who develop acute coronary syndromes are less likely to receive timely revascularization or appropriate medical therapy, despite evidence showing that receipt of these interventions is associated with substantial benefit. Accordingly, a cancer diagnosis is associated with worse outcomes in patients with CAD. The risk-benefit balance of revascularization is becoming more favorable due to the improving prognosis of many cancers and safer revascularization strategies, including shorter requirements for dual antiplatelet therapy after revascularization. SUMMARY: Several factors increase the complexity of managing CAD in patients with cancer. A multidisciplinary approach is recommended to guide treatment decisions in this high-risk and growing patient group.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".