Approach to Cancer-Associated Thrombosis: Challenging Situations and Knowledge Gaps
Bibliographic record
Abstract
Malignancy is a significant risk factor for venous thromboembolism (VTE). It is estimated that up to 20% of patients with cancer may develop VTE at some time in their cancer journey. Cancer-associated VTE can lead to hospitalizations, morbidity, delayed cancer treatment, and mortality. The optimal prevention and management of cancer-associated thrombosis (CAT) is of utmost importance. Direct oral anticoagulants have been recommended as first-line therapy for VTE treatment in the general population and their efficacy has recently been demonstrated in the cancer population, leading to increased use. However, patients with cancer have unique challenges and comorbidities that can lead to increased risks and concerns with anticoagulation. Herein we will discuss commonly encountered challenges in patients with CAT, review available literature, and provide practice suggestions. IMPLICATIONS FOR PRACTICE: This article aims to specifically address cancer-associated thrombosis issues for which there is limited or absent evidence to guide best practice, for circumstances that pose unique challenges for clinicians, and for directions when the literature is conflicting. It reviews pertinent data for each selected topic and provides guidance for patient management based on the best available evidence and experiences from the panel.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".