P253Short-term versus indefinite anticoagulant therapy for secondary prevention of unprovoked venous thromboembolism: a decision analysis
Bibliographic record
Abstract
Background: The optimal duration of anticoagulation for a first unprovoked VTE remain controversial. Methods: A Markov decision analysis model using a cycle length of one year was constructed. The base case [Table] was a hypothetical cohort of men and women with a first unprovoked VTE aged 57 years (average age in management of VTE randomized trials) who had been treated initially for 3 to 6 months with a vitamin K antagonist, and in whom there was clinical equipoise regarding the need for continuing therapy. The model, adopting a lifetime time horizon, assigned patients to either stop anticoagulation or continue treatment indefinitely with a Vitamin K antagonist. Analyses were conducted from a clinical perspective with health outcomes measured by the impact of treatment assignation on life expectancy. Results: Under base case conditions, among patients with a first unprovoked VTE aged 57 years who have completed initial short-term treatment, continuing anticoagulant therapy indefinitely yielded, on average, a net gain in life expectancy for men (46 life-years per 1000 persons), but a net loss in life expectancy for women (-178 life-years per 1000 persons). Importantly, in a 2-way sensitivity analysis [Figure 1; red asterisk=base-case women; black asterisk=base-case men] harms surpassed the benefits of indefinite anticoagulant therapy when the annual major bleeding risk exceeded 1.2%. Conversely, benefits outweighed the harms of indefinite anticoagulation when the risk of recurrent VTE in the first year after stopping treatment was higher than 8.6%.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".