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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".