The win ratio for evaluating edoxaban vs dalteparin for cancer-associated venous thromboembolism: an analysis of the randomized Hokusai Venous Thromboembolism Cancer trial
Bibliographic record
Abstract
BACKGROUND: The Hokusai Venous Thromboembolism (VTE) Cancer trial demonstrated that edoxaban was noninferior to dalteparin for the treatment of cancer-associated venous VTE. OBJECTIVES: We reanalyzed the trial using the win ratio, an approach that evaluates a composite of outcomes in a hierarchical order. METHODS: Forty-nine thrombosis experts ranked 10 outcomes in order of clinical importance from all-cause death (most important) to clinically relevant nonmajor bleeding (least important). We performed unmatched pairwise comparisons between participants on edoxaban and those on dalteparin at 6- and 12-month follow-up. Within each pair, edoxaban was assigned a win, loss, or tie according to the hierarchy of outcomes. We calculated the win ratio (total wins divided by total losses among edoxaban patients), with more wins than losses indicating the benefit of edoxaban, and the win difference (total wins minus total losses). RESULTS: Among 273 528 pairs (522 × 524 participants), edoxaban was associated with a win in 34.9%, a loss in 38.5%, and a tie in 26.6%. The win ratio was 0.91 (95% CI, 0.76-1.08), with a win difference of -3.55% (95% CI, -9.9% to 2.9%) at 12 months. The win ratio remained unchanged at 6 months (0.91; 95% CI, 0.75-1.11). The findings were consistent with a hierarchy of only death, recurrent VTE, and major bleeding (win ratio, 0.92; 95% CI, 0.76-1.11), or when replacing all-cause death with VTE-related death or fatal bleeding (win ratio, 0.83; 95% CI, 0.65-1.06). CONCLUSION: We observed no significant difference between edoxaban and dalteparin for the treatment of cancer-associated VTE when using the win ratio approach with a hierarchy of 10 prioritized outcomes.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".