Management of Cancer-Associated Thrombosis: Unmet Needs and Future Perspectives
Bibliographic record
Abstract
Patients with cancer are at a high risk of symptomatic venous thromboembolism (VTE), which is a common cause of morbidity and mortality in this patient population. Increased risk of recurrent VTE and bleeding complications are two major challenges associated with therapeutic anticoagulation in these patients. Long-term therapy with low-molecular-weight heparins (LMWHs) has been the standard of care for the treatment of cancer-associated VTE given its favorable risk-benefit ratio in comparison with vitamin K antagonists. Direct oral anticoagulants (DOACs), which offer the convenience of oral administration and have a rapid onset of action, have recently emerged as a new treatment option for patients with cancer-associated thrombosis (CT). Randomized clinical trial data with head-to-head comparisons between DOACs and LMWHs showed that overall, DOACs have a similar efficacy profile but a higher risk of bleeding was observed in some of these studies. This review aims to identify unmet needs in the treatment of CT. We discuss important considerations for clinicians tailoring anticoagulation (1) drug-drug interactions, (2) risk of bleeding (e.g., gastrointestinal bleeding), (3) thrombocytopenia, hematological malignancies, (4) metastatic or primary brain tumors, and (5) renal impairment. Additional research is warranted in several clinical scenarios to help clinicians on the best therapeutic approach.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".