Treating cancer-associated venous thromboembolism: A practical approach
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a common and potentially life-threatening complication in patients with cancer. Both cancer and its treatments increase the risk of developing VTE. Specific cancer types and individual patient comorbidities increase the risk of developing cancer-associated VTE, and the risk of bleeding is increased with anticoagulation therapies. The aims of this article are to summarize the latest evidence for treating cancer-associated VTE, discuss the practical considerations involved, and share best practices for VTE treatment in patients with cancer. The article pays particular attention to challenging contexts including patients with brain, lung, gastrointestinal, and genitourinary tumors and those with hematological malignancies. Furthermore, the article summarizes specific clinical scenarios that require additional treatment considerations, including extremes of body weight, nausea and gastrointestinal disturbances, compromised renal function, and anemia, and touches upon the relevance of drug-drug interactions. Historically, vitamin K antagonists and low-molecular-weight heparins (LMWHs) have been used as therapy for cancer-associated VTE. The development of direct oral anticoagulants has provided additional treatment options, which, in certain instances, offer advantages over LMWHs. There are numerous factors that need to be considered when treating cancer-associated VTE, and although various treatment guidelines are helpful, they do not reflect each unique scenario that may arise in clinical practice. This article provides a summary of the latest evidence and a practical approach for treating cancer-associated VTE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".