Cancer‐associated venous thromboembolism: Treatment and prevention with rivaroxaban
Bibliographic record
Abstract
Cancer-associated venous thromboembolism (VTE) is a frequent, potentially life-threatening event that complicates cancer management. Anticoagulants are the cornerstone of therapy for the treatment and prevention of cancer-associated thrombosis (CAT); factor Xa-inhibiting direct oral anticoagulants (DOACs; apixaban, edoxaban, and rivaroxaban), which have long been recommended for the treatment of VTE in patients without cancer, have been investigated in this setting. The first randomized comparisons of DOACs against low-molecular-weight heparin for the treatment of CAT indicated that DOACs are efficacious in this setting, with findings reflected in recent updates to published guidance on CAT treatment. However, the higher risk of bleeding events (particularly in the gastrointestinal tract) with DOACs highlights the need for appropriate patient selection. Further insights will be gained from additional studies that are ongoing or awaiting publication. The efficacy and safety of DOAC thromboprophylaxis in ambulatory patients with cancer at a high risk of VTE have also been assessed in placebo-controlled randomized controlled trials of apixaban and rivaroxaban. Both studies showed efficacy benefits with DOACs, but both studies also showed a nonsignificant increase in major bleeding events while on treatment. This review summarizes the evidence base for rivaroxaban use in CAT, the patient profile potentially most suited to DOAC use, and ongoing controversies under investigation. We also describe ongoing studies from the CALLISTO (Cancer Associated thrombosis-expLoring soLutions for patients through Treatment and Prevention with RivarOxaban) program, which comprises several randomized clinical trials and real-world evidence studies, including investigator-initiated research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".