4320Predictive value of VTE-BLEED to predict major bleeding and other adverse events in a practice-based cohort of patients with venous thromboembolism: results of the XALIA study
Bibliographic record
Abstract
Background: VTE-BLEED (Table 1), a decision tool for predicting major bleeding during stable anticoagulation for venous thromboembolism (VTE), has been shown to reliably identify a patient subgroup at high risk of bleeding. To date, VTE-BLEED has not yet been evaluated in practice-based conditions. Methods: In this validation study, we included 4457 patients previously enrolled in the XALIA study, a multicenter, international, prospective, non-interventional study of patients with confirmed VTE. Only patients who did not meet a study outcome before day 30 were included. We calculated the prognostic indices of the dichotomized VTE-BLEED score for major bleeding after day 30 and day 90, as well as for recurrent VTE and all-cause mortality. Result: The crude Hazard Ratio (HR) for major bleeding after day 30 was 2.6 (95% CI 1.3–5.2) and the treatment-adjusted HR 2.3 (95% CI 1.1–4.5) for the high (versus low) VTE-BLEED score. These numbers 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 in patients with unprovoked VTE was comparable to that of the overall population and was similar in groups stratified by treatment, age and sex. High VTE-BLEED score was associated with higher incidence of all-cause mortality (adjusted HR 11, 95% CI 4.8–23), but not with recurrent VTE (adjusted HR 1.5; 95% CI 0.85–2.7).
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 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".