Risk factors for recurrent venous thromboembolism in the European collaborative paediatric database on cerebral venous thrombosis: a multicentre cohort study
Bibliographic record
Abstract
BACKGROUND: The relative importance of previous diagnosis and hereditary prothrombotic risk factors for cerebral venous thrombosis (CVT) in children in determining risk of a second cerebral or systemic venous thrombosis (VT), compared with other clinical, neuroimaging, and treatment variables, is unknown. METHODS: We followed up the survivors of 396 consecutively enrolled patients with CVT, aged newborn to 18 years (median 5.2 years) for a median of 36 months (maximum 85 months). In accordance with international treatment guidelines, 250 children (65%) received acute anticoagulation with unfractionated heparin or low-molecular weight heparin, followed by secondary anticoagulation prophylaxis with low-molecular weight heparin or warfarin in 165 (43%). RESULTS: Of 396 children enrolled, 12 died immediately and 22 (6%) had recurrent VT (13 cerebral; 3%) at a median of 6 months (range 0.1-85). Repeat venous imaging was available in 266 children. Recurrent VT only occurred in children whose first CVT was diagnosed after age 2 years; the underlying medical condition had no effect. In Cox regression analyses, non-administration of anticoagulant before relapse (hazard ratio [HR] 11.2 95% CI 3.4-37.0; p<0.0001), persistent occlusion on repeat venous imaging (4.1, 1.1-14.8; p=0.032), and heterozygosity for the G20210A mutation in factor II (4.3, 1.1-16.2; p=0.034) were independently associated with recurrent VT. Among patients who had recurrent VT, 70% (15) occurred within the 6 months after onset. CONCLUSION: Age at CVT onset, non-administration of anticoagulation, persistent venous occlusion, and presence of G20210A mutation in factor II predict recurrent VT in children. Secondary prophylactic anticoagulation should be given on a patient-to-patient basis in children with newly identified CVT and at high risk of recurrent VT. Factors that affect recanalisation need further research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".