Effectiveness and Safety of Rivaroxaban and Low Molecular Weight Heparin in Cancer-Associated Venous Thromboembolism
Bibliographic record
Abstract
Background: Direct-acting oral anticoagulants (DOACs) are alternatives to low molecular weight heparin (LMWH) in most cancer-associated thrombosis (CAT) patients. Objectives: This study sought to compare the effectiveness and safety of rivaroxaban and LMWH for venous thromboembolism (VTE) treatment in patients with an active cancer type not associated with a high risk of DOAC bleeding. Methods: An analysis of electronic health records from January 2012 to December 2020 was performed. Patients were adults, had active cancer, experienced an index CAT event, and were treated with rivaroxaban or LMWH. Patients with cancers with an established high risk of bleeding on DOACs were excluded. Baseline covariates were balanced using propensity score-overlap weighting. HRs with 95% CIs were calculated. Results: We identified 3,708 CAT patients treated with rivaroxaban (29.5%) or LMWH (70.5%). The median (25th-75th percentiles) time on anticoagulation was 180 (69-365) and 96 (40-336) days for rivaroxaban and LMWH patients. At 3 months, rivaroxaban was associated with a 31% reduced risk of recurrent VTE vs LMWH (4.2% vs 6.1%; HR: 0.69; 95% CI: 0.51-0.92). No difference in bleeding-related hospitalizations or all-cause mortality was observed (HR: 0.79; 95% CI: 0.55-1.13 and HR: 1.07; 95% CI: 0.85-1.35, respectively). Rivaroxaban reduced the recurrent VTE risk (HR: 0.74; 95% CI: 0.57-0.97) but not bleeding-related hospitalizations or all-cause mortality at 6 months. At 12 months, no difference was observed between cohorts for any of the previously mentioned outcomes. Conclusions: Among active cancer patients experiencing VTE and not at high risk of bleeding on DOACs, rivaroxaban was associated with a reduced risk of recurrent VTE versus LMWHs at 3 and 6 months but not 12 months. (Observational Study in Cancer-Associated Thrombosis for Rivaroxaban-United States Cohort [OSCAR-US]; NCT04979780).
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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".