Treatment and outcomes after on-treatment recurrent venous thromboembolism in patients with cancer: a post hoc analysis of the Hokusai venous thromboembolism cancer study
Bibliographic record
Abstract
BACKGROUND: The management of recurrent venous thromboembolism (VTE) despite anticoagulant treatment in patients with cancer is uncertain. To address this, we used data from the Hokusai VTE Cancer trial, which compared edoxaban with dalteparin to treat cancer-associated VTE. OBJECTIVES: To characterize and evaluate anticoagulant treatment strategies during and after on-treatment recurrent VTE, including the type and dose of anticoagulant. METHODS: In this post hoc analysis, all patients with adjudicated on-treatment recurrent VTE within 12 months after randomization were included. Outcomes were second recurrent VTE and major bleeding within 3 months after the first recurrent VTE. RESULTS: A total of 67 patients developed on-treatment recurrent VTE while receiving therapeutic-dose edoxaban (31%), therapeutic-dose low-molecular-weight heparin (LMWH) (34%), maintenance-dose LMWH (21%), or other therapies (14%). After the recurrent event, 28 patients (42%) received an increased dose, 35 (52%) a comparable dose, and 4 (6%) a reduced dose or stopped anticoagulants. Common treatment regimens included supratherapeutic-dose LMWH (21%), therapeutic-dose LMWH (51%), direct oral anticoagulants (16%), or another treatment strategy (12%). In the 3 months after recurrent VTE, 6 (9%) patients had a second recurrence and 7 (10%) had major bleeding. CONCLUSION: Treatment strategies for recurrent VTE in patients with cancer are heterogeneous. The risk of a second recurrence and major bleeding are considerable. More studies are needed to determine the optimal treatment strategy for recurrent cancer-associated thrombosis.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".