Rivaroxaban Versus Low-Molecular-Weight Heparins in a Broad Cohort of Patients With Cancer-Associated Venous Thromboembolism: An Analysis of the OSCAR-US Program
Bibliographic record
Abstract
Cancer-associated venous thromboembolism (CAT) guidelines recommend direct oral anticoagulants as alternatives to low-molecular-weight heparin (LMWH) in most patients. This study compared the effectiveness and safety of rivaroxaban versus LMWH for a broad CAT cohort. The cohort study used electronic health data from January 2012 to December 2020 to evaluate patients with active cancer experiencing acute venous thromboembolism (VTE) and treated with rivaroxaban or LMWH. Propensity score-overlap weighted hazard ratios (HRs) and 95% confidence intervals (CIs) for VTE, bleeding-related hospitalization, and all-cause mortality were calculated. In total, 4935 patients were identified (27.9% on rivaroxaban and 72.1% on LMWH). The cancer types included gastrointestinal (29.4%), genitourinary (26.2%), lung (24.0%), breast (19.7%), and hematologic (14.4%). Rivaroxaban was associated with a reduction in recurrent VTE versus LMWH among all patients with cancer (HR = 0.78; 95%CI = 0.61-0.99) at 3 months. No differences in bleeding-related hospitalization or all-cause mortality were observed. Directionally similar results to those at 3 months were observed at 6 months for all outcomes. In conclusion, we observed fewer recurrent VTE cases and no increase in bleeding-related hospitalizations with rivaroxaban versus LMWH at 3 months in this patient cohort with various cancer types.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".