Risk of recurrent venous thromboembolism or bleeding in relation to thrombophilic risk factors in patients receiving ximelagatran or placebo for long‐term secondary prevention of venous thromboembolism
Bibliographic record
Abstract
The impact of prothrombotic abnormalities on the risk of recurrent venous thromboembolism (VTE) and bleeding in patients receiving long-term anticoagulation remains unclear. This analysis evaluated the influence of potential prothrombotic risk factors (antithrombin, protein C, protein S, factor V Leiden mutation, prothrombin gene G20210A mutation, cardiolipin antibodies, number of risk factors) on the risk of recurrent VTE or bleeding during treatment with oral ximelagatran (24 mg twice daily) or placebo for 18 months [THRombin Inhibitor in Venous thromboEmbolism (THRIVE) III trial]. Of the 1223 patients in the intention-to-treat population, prothrombotic state was analysed in 559 patients receiving ximelagatran and 540 patients receiving placebo. It is possible that patients at a high risk of recurrent VTE were poorly represented in this analysis because of selection bias. Prothrombotic risk factors were reported in 41% of patients (8% had > or = 2 factors). No significant interactions were found between ximelagatran treatment and potential prothrombotic risk factors for the risk of recurrent VTE or bleeding by Cox proportionate hazard modelling. There was no clear evidence for a higher risk of recurrent VTE or bleeding across subgroups according to the potential prothrombotic factors analysed in this study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".