Rivaroxaban versus Apixaban for Treatment of Cancer-Associated Venous Thromboembolism in Patients at Lower Risk of Bleeding
Bibliographic record
Abstract
This retrospective study, utilizing U.S. electronic health record (EHR) data from January 2013 to December 2020, sought to assess whether rivaroxaban and apixaban had similar effectiveness and safety in the treatment of cancer-associated venous thromboembolism (VTE) in patients with a cancer type not associated with a high risk of bleeding. We included adults diagnosed with active cancer, excluding esophageal, gastric, unresected colorectal, bladder, noncerebral central nervous system cancers and leukemia, who experienced VTE and received a therapeutic VTE dose of rivaroxaban or apixaban on day 7 post-VTE, and were active in the EHR ≥12 months prior to the VTE. Primary outcome was the composite of recurrent VTE or any bleed resulting in hospitalization at 3 months. Secondary outcomes included recurrent VTE, any bleed resulting in hospitalization, any critical organ bleed, and composites of these outcomes at 3 and 6 months. Inverse probability of treatment-weighted Cox regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). We included 1,344 apixaban and 1,093 rivaroxaban patients. At 3 months, rivaroxaban was found to have similar hazard to apixaban for developing recurrent VTE or any bleed resulting in hospitalization (HR: 0.87; 95% CI: 0.60–1.27). No differences were observed between cohorts for this outcome at 6 months (HR: 1.00; 95% CI: 0.71–1.40) or for any other outcome at 3 or 6 months. In conclusion, patients receiving rivaroxaban or apixaban showed similar risks of the composite of recurrent VTE or any bleed resulting in hospitalization in patients with cancer-associated VTE. This study was registered at www.clinicaltrials.gov as #NCT05461807. Key Points
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".