Net clinical benefit of dabigatran vs. warfarin in venous thromboembolism: analyses from RE-COVER®, RE-COVER™ II, and RE-MEDY™
Bibliographic record
Abstract
The direct oral anticoagulants, e.g., dabigatran etexilate (DE), are effective and well tolerated treatments for venous thromboembolism (VTE). Net clinical benefit (NCB) is a useful concept in weighing potential benefits against potential harm of comparator drugs. The NCB of DE vs. warfarin in VTE treatment was compared. Post-hoc analyses were performed on pooled data from the 6-month RE-COVER® and RE-COVER™ II trials, and data from the RE-MEDY™ trial (up to 36 months), to compare the NCB of DE (150 mg twice daily) and warfarin [target international normalized ratio (INR) 2.0-3.0]. Patients (≥18 years old) had symptomatic proximal deep vein thrombosis and/or pulmonary embolism. NCB was the composite of cardiovascular endpoints (non-fatal events of recurrent VTE, myocardial infarction, stroke or systemic embolism), all-cause death, and bleeding outcomes, all weighted equally. A broad definition of NCB included major bleeding events (MBE) and clinically relevant non-major bleeding events as bleeding outcomes, while a narrow definition included just MBE. The pooled dataset totalled 5107 patients from RE-COVER/RE-COVER II and 2856 patients from RE-MEDY. When NCB was narrowly defined, NCB was similar between DE and warfarin. When broadly defined, NCB was superior with DE vs. warfarin [RE-COVER/RE-COVER II, hazard ratio (HR) 0.80; 95% confidence interval (CI), 0.68-0.95 and RE-MEDY, HR 0.73; 95% CI 0.59-0.91]. These findings were unaffected by warfarin time in therapeutic range. The NCB of DE was similar or superior to warfarin, depending on the NCB definition used, regardless of the quality of INR control.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| 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 | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".