Edoxaban for treatment of venous thromboembolism in patients with cancer
Bibliographic record
Abstract
Direct oral anticoagulants may be effective and safe for treatment of venous thromboembolism (VTE) in cancer patients, but they have not been compared with low-molecular-weight heparin (LMWH), the current recommended treatment for these patients. The Hokusai VTE-cancer study is a randomised, open-label, clinical trial to evaluate whether edoxaban, an oral factor Xa inhibitor, is non-inferior to LMWH for treatment of VTE in patients with cancer. We present the rationale and some design features of the study. One such feature is the composite primary outcome of recurrent VTE and major bleeding during a 12-month study period. These two complications occur frequently in cancer patients receiving anticoagulant treatment and have a significant impact. The evaluation beyond six months will fill the current gap in the evidence base for the long-term treatment of these patients. Based on the observation that the risk of recurrent VTE in patients with active cancer is similar to that in those with a history of cancer, the Hokusai VTE-cancer study will enrol patients if whose cancer was diagnosed within the past two years. In addition, patients with incidental VTE are eligible because their risk of recurrent VTE is similar to that in patients with symptomatic disease. The unique design features of the Hokusai VTE-cancer study should lead to enrolment of a broad spectrum of cancer patients with VTE who could benefit from oral anticoagulant treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".