Management of cancer-associated venous thromboembolism: Perspectives on optimizing current therapeutics with a focus on factor XI inhibition
Bibliographic record
Abstract
Cancer-associated thrombosis (CAT) encompasses manifestations of deep venous thrombosis and/or pulmonary embolism occurring during the evolution of cancer. CAT represents one of the major cardiovascular complications associated with cancer and anti-cancer treatments, and the second leading cause of death after cancer progression. The rate of venous thromboembolism (VTE) recurrence is augmented in patients with cancer, together with the risk of bleeding, when compared with subjects without malignancy. Thus, decisions on optimal anticoagulation strategy should carefully balance both thrombotic and bleeding risk. While low-molecular weight heparins and direct oral anticoagulants now represent the standard-of-care in patients with cancer, newer pharmacologic compounds able to prevent VTE recurrence while minimizing the hemorrhagic risk are needed, and currently under investigation. In particular, factor XI inhibitors have emerged as potentially safe drugs in this highly vulnerable population, although results from dedicated clinical trials are waited to confirm this hypothesis. This review aims to summarize current management, controversies, and latest developments in pharmacotherapeutic approaches for patients with CAT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".