Comparison of Clinical Outcomes in Patients with Active Cancer Receiving Rivaroxaban or Low-Molecular-Weight Heparin: The OSCAR-UK Study
Bibliographic record
Abstract
BACKGROUND: In most patients with cancer-associated venous thromboembolism (CT), essentially those not at high risk of bleeding, guidelines recommend treatment with direct oral anticoagulants as an alternative to low-molecular-weight heparins (LMWHs). Population-based studies comparing these therapies are scarce. OBJECTIVES: To compare the risk of venous thromboembolism (VTE) recurrences, significant bleeding, and all-cause mortality in patients with CT receiving rivaroxaban or LMWHs. PATIENTS/METHODS: Using UK Clinical Practice Research Datalink data from 2013 to 2020, we generated a cohort of patients with first CT treated initially with either rivaroxaban or LMWH. Patients were observed 12 months for VTE recurrences, significant bleeds (major bleeds or clinically relevant nonmajor bleeding requiring hospitalization), and all-cause mortality. Overlap weighted sub-distribution hazard ratios (SHRs) compared rivaroxaban with LMWH in an intention-to-treat analysis. RESULTS: The cohort consisted of 2,259 patients with first CT, 314 receiving rivaroxaban, and 1,945 LMWH, mean age 72.4 and 66.9 years, respectively. In the 12-month observational period, 184 person-years following rivaroxaban and 1,057 following LMWH, 10 and 66 incident recurrent VTE events, 20 and 102 significant bleeds, and 10 and 133 deaths were observed in rivaroxaban and LMWH users, respectively. The weighted SHR at 12 months for VTE recurrences in rivaroxaban compared with LMWH were 0.80 (0.37-1.73); for significant bleeds 1.01 (0.57-1.81); and for all-cause mortality 0.49 (0.23-1.06). CONCLUSION: Patients with CT, not at high risk of bleeding, treated with either rivaroxaban or LMWH have comparable effectiveness and safety outcomes. This supports the recommendation that rivaroxaban is a reasonable alternative to LMWH for the treatment of CT.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".