Prediction of bleeding events in patients with venous thromboembolism on stable anticoagulation treatment
Bibliographic record
Abstract
Attempts at identifying patients with an elevated risk of bleeding while on anticoagulation following acute venous thromboembolism (VTE) have largely been unsuccessful thus far. We sought to develop a clinical prediction score for bleeding during stable anticoagulation treatment after acute VTE.We performed a post hoc analysis of the pooled RE-COVER studies, two double-blind randomised "sister" trials evaluating dabigatran versus standard treatment in 5107 VTE patients.A score was derived from patients randomised to dabigatran using logistic regression analysis covering the complete follow-up period. The final model, named VTE-BLEED, included six variables and yielded a c-statistic of 0.72 (95% CI 0.67-0.76). Patients from the derivation cohort in the low-risk group (<2 points; 74% of the derivation population) had a bleeding incidence of 2.8% compared to 12.6% in the elevated-risk group (OR 5.0; 95% CI 3.5-7.1). The score proved accurate for our primary end-point, i.e. prediction of major bleeding after day 30 ("stable" anticoagulation), both in patients on dabigatran (c-statistic 0.75, 95% CI 0.61-0.89) and those on warfarin (0.78, 95% CI 0.68-0.86; p=0.77 for difference).The new VTE-BLEED score accurately predicted major bleeding events in VTE patients on stable anticoagulation with both dabigatran and warfarin.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".