Influence of preceding length of anticoagulant treatment and initial presentation of venous thromboembolism on risk of recurrence after stopping treatment: analysis of individual participants' data from seven trials
Bibliographic record
Abstract
OBJECTIVE: To determine how length of anticoagulation and clinical presentation of venous thromboembolism influence the risk of recurrence after anticoagulant treatment is stopped and to identify the shortest length of anticoagulation that reduces the risk of recurrence to its lowest level. DESIGN: Pooled analysis of individual participants' data from seven randomised trials. SETTING: Outpatient anticoagulant clinics in academic centres. POPULATION: 2925 men or women with a first venous thromboembolism who did not have cancer and received different durations of anticoagulant treatment. MAIN OUTCOME MEASURE: First recurrent venous thromboembolism after stopping anticoagulant treatment during up to 24 months of follow-up. RESULTS: Recurrence was lower after isolated distal deep vein thrombosis than after proximal deep vein thrombosis (hazard ratio 0.49, 95% confidence interval 0.34 to 0.71), similar after pulmonary embolism and proximal deep vein thrombosis (1.19, 0.87 to 1.63), and lower after thrombosis provoked by a temporary risk factor than after unprovoked thrombosis (0.55, 0.41 to 0.74). Recurrence was higher if anticoagulation was stopped at 1.0 or 1.5 months compared with at 3 months or later (hazard ratio 1.52, 1.14 to 2.02) and similar if treatment was stopped at 3 months compared with at 6 months or later (1.19, 0.86 to 1.65). High rates of recurrence associated with shorter durations of anticoagulation were confined to the first 6 months after stopping treatment. CONCLUSION: Three months of treatment achieves a similar risk of recurrent venous thromboembolism after stopping anticoagulation to a longer course of treatment. Unprovoked proximal deep vein thrombosis and pulmonary embolism have a high risk of recurrence whenever treatment is stopped.
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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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".