Predictors of thrombosis in relatives of patients with venous thromboembolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hereditary and acquired risk factors contribute to the development of venous thromboembolism (VTE). Relatives of patients who have a hereditary predisposition to thrombosis ('hereditary thrombophilia') will have a heightened risk of VTE if they share the same thrombophilic abnormality. However, if patients do not have a currently recognized thrombophilia it cannot be assumed that their relatives have a normal risk of thrombosis; the patient and their relatives may have a yet to be discovered hereditary abnormality. RECENT FINDINGS: A recent study by our group suggests that the relatives of patients with unprovoked VTE at a young age (e.g. <45 years) have a substantially higher risk [odds ratio (OR) 3.3; 95% confidence interval (CI) 1.7-6.4] of developing VTE than the relatives of older patients with unprovoked VTE (e.g. >71 years). This effect occurred independently of whether patients had a currently recognized hereditary thrombophilia. SUMMARY: Relatives of patients with unprovoked VTE have an increased risk of VTE. Testing for thrombophilia in the patients, and in the relatives of the patients who have a thrombophilia, allows the risk of thrombosis in the relatives to be estimated. Thrombosis at a young age appears to be an additional independent risk factor for a heightened risk of thrombosis in patients' relatives.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".