Healthcare resource use in XALIA: A subgroup analysis of a non-interventional study of rivaroxaban versus standard anticoagulation for deep vein thrombosis
Bibliographic record
Abstract
INTRODUCTION: The non-interventional XALIA study compared the safety and effectiveness of rivaroxaban with standard anticoagulation for the treatment of venous thromboembolism in routine clinical practice. This substudy assessed the effect of treatment with rivaroxaban on healthcare resource use, hospital length of stay (LOS) and frequency of hospitalisation. METHODS: In XALIA, patients aged ≥18 years scheduled to receive ≥3 months of rivaroxaban or standard anticoagulation treatment for deep vein thrombosis (DVT) were eligible. Treatment decisions were at the physician's discretion. Healthcare resource use, including hospital admission for the index DVT and initial LOS, was documented. The main analyses in this substudy were conducted in a 1:1 propensity score-matched set (PMS) of patients, with adjustment for cancer at baseline. RESULTS: In the PMS analysis, 1124 rivaroxaban-treated patients and 1124 standard anticoagulation-treated patients were included. Baseline characteristics were similar between groups (mean age 60.8 years vs. 61.2 years, DVT only rates of 89.7% vs. 90.2% and cancer rates of 8.4% vs. 8.5%, respectively). Of these, 433/1124 (38.5%) rivaroxaban-treated patients and 438/1124 (39.0%) standard anticoagulation-treated patients were hospitalised. Index event LOS in the PMS analysis was a least-squares mean of 2.6 days shorter with rivaroxaban vs. standard anticoagulation (5.4 vs. 8.0 days; geometric means ratio = 0.67 [95% confidence interval 0.61-0.74, P < 0.001]). CONCLUSIONS: In XALIA, hospital LOS was shorter with rivaroxaban than with standard anticoagulation, consistent with the phase III study results. DVT treatment with rivaroxaban in routine clinical practice may reduce the cost per patient vs. standard anticoagulation.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".