Predicting major bleeding during extended anticoagulation for unprovoked or weakly provoked venous thromboembolism
Bibliographic record
Abstract
No clinical prediction model has been specifically developed or validated to identify patients with unprovoked venous thromboembolism (VTE) who are at high risk of major bleeding during extended anticoagulation. In a prospective multinational cohort study of patients with unprovoked VTE receiving extended anticoagulation after completing ≥3 months of initial treatment, we derived a new clinical prediction model using a multivariable Cox regression model based on 22 prespecified candidate predictors for the primary outcome of major bleeding. This model was then compared with modified versions of 5 existing clinical scores. A total of 118 major bleeding events occurred in 2516 patients (annual risk, 1.7%; 95% confidence interval [CI], 1.4-2.1). The incidences of major bleeding events per 100 person-years in high-risk and non-high-risk patients, respectively, were 3.9 (95% CI, 3.0-5.1) and 1.1 (0.8-1.4) using the newly derived creatinine, hemoglobin, age, and use of antiplatelet agent (CHAP) model; 3.3 (2.6-4.1) and 1.0 (0.7-1.3) using modified ACCP score, 5.3 (0.6-19.2) and 1.7 (1.4-2.0) using modified RIETE score, 3.1 (2.3-3.9) and 1.1 (0.9-1.5) using modified VTE-BLEED score, 5.2 (3.3-7.8) and 1.5 (1.2-1.8) using modified HAS-BLED score, and 4.8 (1.3-12.4) and 1.7 (1.4-2.0) using modified outpatient bleeding index score. Modified versions of the ACCP, VTE-BLEED, and HAS-BLED scores help identify patients with unprovoked VTE who are at high risk of major bleeding and should be considered for discontinuation of anticoagulation after 3 to 6 months of initial treatment. The CHAP model may further improve estimation of bleeding risk by using continuous predictor variables, but external validation is required before its implementation in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".