Patient-Reported Treatment Experience with Oral Rivaroxaban: Results from the Noninterventional XALIA Study of Deep-Vein Thrombosis
Bibliographic record
Abstract
Abstract For venous thromboembolism (VTE) treatment, patient satisfaction was shown to improve with rivaroxaban versus standard anticoagulation in the phase III EINSTEIN DVT and EINSTEIN PE trials. This substudy of the prospective, noninterventional XALIA study of rivaroxaban for deep-vein thrombosis treatment assessed if this was also observed in routine clinical practice. Patients enrolled in XALIA who received rivaroxaban or standard anticoagulation treatment were eligible for inclusion in this substudy. Treatment decisions were at the physician's discretion. Patients completed the 17-item Anti-Clot Treatment Scale (ACTS, comprising a 12-item Burdens subscale, a 3-item Benefits subscale and one global item per subscale) during follow-up. The propensity score-matched set (PMS) was used for the main analysis; the adjusted safety analysis (ASAF) set was used for confirmatory purposes. Analyses by follow-up visit and subgroup, including age, sex, and previous VTE, were also conducted. The PMS-ACTS analysis included 458 rivaroxaban-treated and 434 standard anticoagulation-treated patients. Baseline demographic and clinical characteristics were generally similar across treatment arms. ACTS Burdens scores significantly improved with rivaroxaban versus standard anticoagulation (least-squares mean difference of 2.4 ± 0.4 points; p < 0.0001); ACTS Benefits scores were numerically higher with rivaroxaban (least-squares mean difference of 0.2 ± 0.1 points; p = 0.2). Similar findings occurred across follow-up visits and subgroups. Results were confirmed in the ASAF-ACTS analysis. Consistent with phase III analyses, rivaroxaban was associated with improved ACTS Burdens scores; ACTS Benefits scores numerically favored rivaroxaban, although without reaching statistical significance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".