Incidence of venous thromboembolism in first-degree relatives of patients with venous thromboembolism who have factor V Leiden
Bibliographic record
Abstract
The factor V Leiden (FVL) mutation, a genetic abnormality with an autosomal mode of inheritance, is associated with an increased risk of venous thromboembolism (VTE). We aimed to determine the annual incidence of VTE in first-degree relatives of patients with VTE and FVL and to identify factors in patients and the relatives that influence this incidence. In this retrospective and prospective cohort study, the incidence of objectively diagnosed first episodes of VTE was assessed in 553 first-degree relatives of 161 patients with acute VTE and FVL. The annual incidence of VTE was 0.43% (95% CI, 0.3 to 0.56) with FVL and 0.17% (95% CI, 0.07 to 0.27) without FVL (relative risk of 2.5,95% CI, 1.3 to 4.7). A majority (70%) of episodes of VTE were provoked, and this proportion was similar with and without FVL. A larger proportion of VTE was provoked in women (83%) that in men (33%), with the difference accounted for by pregnancy and use of oral contraceptives. The proportion of pregnancies complicated by VTE was 3.9% (95% CI, 2.0-5.8) with FVL and 1.4% (95% CI, 0.04-2.7) without FVL. FVL is associated with a two- to threefold increase in VTE in first-degree relatives of patients with VTE. No subgroup of relatives was identified who require more than routine prophylaxis because of a particularly high risk of VTE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".