Subgroup Analysis of Patients with Cancer in XALIA: A Noninterventional Study of Rivaroxaban versus Standard Anticoagulation for VTE
Bibliographic record
Abstract
Background The noninterventional XALIA study compared rivaroxaban with standard anticoagulation for deep vein thrombosis treatment. This substudy describes the demographics, clinical characteristics, and outcomes of the patients with cancer. Methods Therapy type, dose, and duration were at the physician's discretion. The cohorts identified were rivaroxaban (rivaroxaban alone or after heparin or fondaparinux for ≤48 hours); early switchers (rivaroxaban after heparin or fondaparinux for >48 hours to 14 days and/or a vitamin K antagonist [VKA] for 1–14 days); standard anticoagulation (heparin or fondaparinux and a VKA); low-molecular-weight heparin (LMWH) alone; and miscellaneous (other heparins, fondaparinux alone, VKA alone). Primary outcomes were major bleeding, recurrent venous thromboembolism, and all-cause mortality. Results In XALIA, 587 patients (11.4% of the XALIA cohort) were with cancer: 146 (24.9%) rivaroxaban, 30 (5.1%) early switchers, 141 (24.0%) standard anticoagulation, 223 (38.0%) LMWH, and 47 (8.0%) miscellaneous. Patients with gastrointestinal or lung cancer more commonly received LMWH than rivaroxaban; the opposite occurred in patients with breast or genitourinary cancer. Rates of primary outcome in the rivaroxaban group were as follows: major bleeding, 1.4% (n = 2); recurrent venous thromboembolism, 3.4% (n = 5); and all-cause mortality, 4.8% (n = 7). Conclusion In XALIA, physicians treated cancer-associated thrombosis with various anticoagulant regimens, most commonly LMWH. In addition, the choice of anticoagulant varied with cancer type. In rivaroxaban-treated patients, rates for the primary outcomes were low, suggesting that patients administered rivaroxaban were a good prognosis group.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| 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) | 0.002 | 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".