Management of bleeding risk in patients who receive anticoagulant therapy for venous thromboembolism: Communication from the ISTH SSC Subcommittee on Predictive and Diagnostic Variables in Thrombotic Disease
Bibliographic record
Abstract
Patients with acute venous thromboembolism (VTE) require anticoagulant therapy to prevent recurrent VTE and death, which exposes them to an inherent increased risk of bleeding. Identification of patients at high risk of bleeding, and mitigating this risk, is an essential component of the immediate and long-term therapeutic management of VTE. The bleeding risk can be estimated by either implicit judgment, weighing individual predictors (clinical variables or biomarkers), or by risk prediction tools developed for this purpose. Management of bleeding risk in clinical practice is, however, far from standardized. International guidelines are contradictory and lack clear and consistent guidance on the optimal management of bleeding risk. This report of the ISTH subcommittee on Predictive and Diagnostic Variables in Thrombotic Disease summarizes the evidence on the prediction of bleeding in VTE patients. We systematically searched the literature and identified 34 original studies evaluating either predictors or risk prediction models for prediction of bleeding risk on anticoagulation in VTE patients. Based on this evidence, we provide recommendations for the standardized management of bleeding risk in VTE patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".