Management of Patients With Unprovoked Venous Thromboembolism: An Evidence-Based and Practical Approach
Bibliographic record
Abstract
OPINION STATEMENT: The management of patients with unprovoked venous thromboembolism is a common and challenging clinical problem. Although the initial antithrombotic management is well-established, there is uncertainty about the optimal long-term anticoagulant management, specifically whether patients should receive a short (i.e., 3- to 6-month) duration of anticoagulant therapy or indefinite anticoagulation. Factors that may be considered to estimate patients' risk for recurrent thromboembolism include the mode of initial clinical presentation, as deep vein thrombosis or pulmonary embolism, patient sex, antecedent hormonal therapy use, thrombophilia, D-dimer levels, and residual vein occlusion in patients with deep vein thrombosis. Many of these factors have been integrated into clinical prediction guides which stratify patients with unprovoked venous thromboembolism according to their risk for disease recurrence and, thereby, can assist clinicians in decisions about the duration of anticoagulation. The objective of this review is to consider the evidence relating to the clinical significance of purported risk factors and provide a practical case-based approach to guide decisions on duration of anticoagulation for patients with unprovoked venous thromboembolism.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".