Factor XI inhibitors: a new option for the prevention and treatment of cancer-associated thrombosis
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a relatively common complication in cancer patients with potentially dire consequences. Anticoagulants are the mainstay of treatment of cancer-associated VTE. The anticoagulants most often used are low-molecular-weight heparin (LMWH) and direct oral factor (F) Xa inhibitors, which include apixaban, edoxaban, and rivaroxaban. Most guidelines recommend primary VTE prophylaxis with LMWH, apixaban, or rivaroxaban after abdominal or pelvic cancer surgery, or in high-risk ambulatory cancer patients. Both oral FXa inhibitors and LMWH have limitations. LMWH requires daily subcutaneous injections, and because of its renal clearance, its use may be problematic in patients with severe kidney disease. The risk of bleeding with oral FXa inhibitors may be higher than with LMWH in patients with intraluminal gastrointestinal or genitourinary cancers. Other problems with oral FXa inhibitors include potential drug-drug interactions and dosing issues in patients with thrombocytopenia or severe kidney or liver disease. Therefore, there remains a need for convenient and safer anticoagulants for VTE treatment in cancer patients. FXI has emerged as a potentially safer target for anticoagulants than FXa because FXI is essential for thrombosis, but mostly dispensable for hemostasis. This review summarizes the currently available therapeutic options for cancer-associated VTE, highlights knowledge gaps, and discusses the potential of FXI inhibitors to address key unmet clinical needs in this vulnerable patient population.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".