Comparison of rivaroxaban and low molecular weight heparin in the treatment of cancer-associated venous thromboembolism: a Swedish national population-based register study
Bibliographic record
Abstract
BACKGROUND: Treating cancer-associated venous thromboembolism (CAT) with anticoagulation prevents recurrent venous thromboembolism (rVTE), but increases bleeding risk. OBJECTIVES: To compare incidence of rVTE, major bleeding, and all-cause mortality for rivaroxaban versus low molecular weight heparin (LMWH) in patients with CAT. METHODS: We developed a cohort study using Swedish national registers 2013-2019. Patients with CAT (venous thromboembolism within 6 months of cancer diagnosis) were included. Those with other indications or with high bleeding risk cancers were excluded (according to guidelines). Follow-up was from index-CAT until outcome, death, emigration, or end of study. Incidence rates (IR) per 1000 person-years with 95% confidence interval (CI) and propensity score overlap-weighted hazard ratios (HRs) for rivaroxaban versus LMWH were estimated. RESULTS: We included 283 patients on rivaroxaban and 5181 on LMWH. The IR for rVTE was 68.7 (95% CI 40.0-109.9) for rivaroxaban, compared with 91.6 (95% CI 81.9-102.0) for LMWH, with adjusted HR 0.77 (95% CI 0.43-1.35). The IR for major bleeding was 23.5 (95% CI 8.6-51.1) for rivaroxaban versus 49.2 (95% CI 42.3-56.9) for LMWH, with adjusted HR 0.62 (95% CI 0.26-1.49). The IR for all-cause mortality was 146.8 (95% CI 103.9-201.5) for rivaroxaban and 565.6 (95% CI 541.8-590.2) for LMWH with adjusted HR 0.48 (95% CI 0.34-0.67). CONCLUSIONS: Rivaroxaban performed similarly to LMWH for patients with CAT for rVTE and major bleeding. An all-cause mortality benefit was observed for rivaroxaban which potentially may be attributed to residual confounding. TRIAL REGISTRATION NUMBER: NCT05150938 (Registered 9 December 2021).
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".