Recurrent stroke: the role of thrombophilia in a large international pediatric stroke population
Bibliographic record
Abstract
Risk factors for arterial ischemic stroke in children include vasculopathy and prothrombotic risk factors but their relative importance to recurrent stroke is uncertain. Data on recurrent stroke from databases held in Canada (Toronto), Germany (Kiel-Lübeck/Münster), and the UK (London/Southampton) were pooled. Data were available from 894 patients aged 1 month to 18 years at first stroke (median age, 6 years) with a median follow-up of 35 months. Among these 894 patients, 160 (17.9%) had a recurrence between 1 day and 136 months after the first stroke (median, 3.1 months). Among 288 children with vasculopathy, recurrence was significantly more common [hazard ratio (HR) 2.5, 95% confidence interval (95% CI) 1.92-3.5] compared to the rate in children without vasculopathy. Adjusting for vasculopathy, isolated antithrombin deficiency (HR 3.9; 95% CI: 1.4-10.9), isolated elevated lipoprotein (a) (HR 2.3; 95% CI: 1.3-4.1), and the presence of more than one prothrombotic risk factor (HR 1.9; 95% CI: 1.12-3.2) were independently associated with an increased risk of recurrence. Recurrence rates calculated per 100 person-years were 10 (95% CI: 3-24) for antithrombin deficiency, 6 (95% CI: 4-9) for elevated lipoprotein (a), and 13 (95% CI: 7-20) for the presence of more than one prothrombotic risk factor. Identifying children at increased risk of a second stroke is important in order to intensify measures aimed at preventing such recurrences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".