Drug-drug interactions in anticoagulant therapy: a focus on oncology patients
Bibliographic record
Abstract
INTRODUCTION: Patients with cancer are frequently exposed to polypharmacy, carrying a high risk of drug-drug interactions (DDIs). Given their substantial risk for thrombosis and atrial fibrillation, anticoagulants are commonly prescribed in this population. Anticoagulants can be associated with relevant pharmacokinetic and pharmacodynamic DDIs, yet their clinical significance remains undetermined. AREAS COVERED: A literature search of preclinical and clinical studies was performed to identify DDIs between anticoagulation and anticancer treatment, with an emphasis on pharmacokinetic and pharmacodynamic mechanisms. This narrative review summarizes the general principles of DDIs involving anticoagulation in patients with cancer. A comprehensive review of DDIs between anticoagulants and different classes of anticancer therapies is provided, including recent outcome studies evaluating their impact on mortality, clinically relevant bleeding, and thrombosis. EXPERT OPINION: Based on available data, we propose a practical approach to DDI assessment and clinical interpretation to support decision-making in patients with cancer requiring anticoagulation. This includes systematic screening for DDIs, tailoring anticoagulants during both initial and long-term treatment (primarily in the context of venous thromboembolism), providing patient counseling based on available evidence, and involving the pharmacy team in complex cases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".