Vascular Complications of Cancer Chemotherapy
Bibliographic record
Abstract
Development of new anticancer drugs has resulted in improved mortality rates and 5-year survival rates in patients with cancer. However, many of the modern chemotherapies are associated with cardiovascular toxicities that increase cardiovascular risk in cancer patients, including hypertension, thrombosis, heart failure, cardiomyopathy, and arrhythmias. These limitations restrict treatment options and might negatively affect the management of cancer. The cardiotoxic effects of older chemotherapeutic drugs such as alkylating agents, antimetabolites, and anticancer antibiotics have been known for a while. The newer agents, such as the antiangiogenic drugs that inhibit vascular endothelial growth factor signalling are also associated with cardiovascular pathology, especially hypertension, thromboembolism, myocardial infarction, and proteinuria. Exact mechanisms by which vascular endothelial growth factor inhibitors cause these complications are unclear but impaired endothelial function, vascular and renal damage, oxidative stress, and thrombosis might be important. With increasing use of modern chemotherapies and prolonged survival of cancer patients, the incidence of cardiovascular disease in this patient population will continue to increase. Accordingly, careful assessment and management of cardiovascular risk factors in cancer patients by oncologists and cardiologists working together is essential for optimal care so that prolonged cancer survival is not at the expense of increased cardiovascular events.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".