Predictive value of venous thromboembolism (<scp>VTE</scp>)‐<scp>BLEED</scp> to predict major bleeding and other adverse events in a practice‐based cohort of patients with <scp>VTE</scp>: results of the <scp>XALIA</scp> study
Bibliographic record
Abstract
Venous thromboembolism (VTE)-BLEED, a decision tool for predicting major bleeding during chronic anticoagulation for VTE has not yet been validated in practice-based conditions. We calculated the prognostic indices of VTE-BLEED for major bleeding after day 30 and day 90, as well as for recurrent VTE and all-cause mortality, in 4457 patients enrolled in the international, prospective XALIA study. The median at-risk time was 190 days (interquartile range 106-360). The crude hazard ratio (HR) for major bleeding after day 30 was 2·6 [95% confidence interval (CI) 1·3-5·2] and the treatment-adjusted HR was 2·3 (95% CI 1·1-4·5) for VTE-BLEED high (versus low) risk patients: the corresponding values for major bleeding after day 90 were 3·8 (95% CI 1·6-9·3) and 3·2 (95% CI 1·3-7·7), respectively. The predictive value of VTE-BLEED was similar in selected patients with unprovoked VTE or those treated with rivaroxaban. High VTE-BLEED score was associated with higher incidence of all-cause mortality (treatment-adjusted HR 11, 95% CI 4·8-23), but not evidently with recurrent VTE (treatment-adjusted HR 1·5; 95% CI 0·85-2·7). These results confirm the predictive value of VTE-BLEED in practice-based data in patients treated with rivaroxaban or conventional anticoagulation, supporting the hypothesis that VTE-BLEED may be useful for making management decisions on the duration of anticoagulant therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".