Recent Advances in the Management of Cancer-Associated Thrombosis: New Hopes but New Challenges
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a common cause of morbidity and mortality in cancer patients and leads to a significant increase in health care costs. Cancer patients often suffer from multiple co-morbidities and have both a greater risk of VTE recurrence and bleeding compared to non-cancer patients. Anticoagulation is therefore challenging. For many years, long-term therapy with Low-Molecular-Weight Heparin (LMWH) was the standard of care for the management of cancer-associated VTE. Direct oral anticoagulants (DOAC), which offer the convenience of an oral administration and have a rapid onset of action, have recently been proposed as a new option in this setting. Head-to-head comparisons between DOAC and LMWHs for the treatment of established VTE are now available, and data on the efficacy and safety of these drugs for primary prophylaxis of VTE in ambulatory cancer patients receiving systemic anticancer therapy are emerging. This narrative review aims to summarize the main recent advances in the prevention and treatment of cancer-associated VTE, including recent data on the use of individualized factors to stratify the risk of VTE in each individual patient, quality-of-life in patients treated with LMWH, and the place that DOACs will likely take in the cancer-associated VTE management landscape.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".