Prolonged or recurrent acute seizures after pediatric arterial ischemic stroke are associated with increasing epilepsy risk
Bibliographic record
Abstract
AIM: To determine epilepsy risk factors after pediatric stroke. METHOD: A cohort of children with arterial ischemic stroke (birth-18y) was enrolled at 21 centers and followed for 1 year. Acute seizures (≤7d after stroke) and active epilepsy (at least one unprovoked remote seizure plus maintenance anticonvulsant at 1y) were identified. Predictors were determined using logistic regression. RESULTS: Among 114 patients (28 neonates and 86 children) enrolled, 26 neonates (93%) and 32 children (37%) had an acute seizure. Acute seizures lasted longer than 5 minutes in 23 patients (40%) and were frequently recurrent: 33 (57%) had 2 to 10 seizures and 11 (19%) had more than 10. Among 109 patients with 1-year follow-up, 11 (10%) had active epilepsy. For each year younger, active epilepsy was 20% more likely (odds ratio [OR] 0.8, 95% confidence interval [CI] 0.6-0.99, p=0.041). Prolonged or recurrent acute seizures also increased epilepsy risk. Each additional 10 minutes of the longest acute seizure increased epilepsy risk fivefold (OR 4.7, 95% CI 1.7-13). Patients with more than 10 acute seizures had a 30-fold increased epilepsy risk (OR 30, 95% CI 2.9-305). INTERPRETATION: Pediatric stroke survivors, especially younger children, have a high risk of epilepsy 1 year after stroke. Prolonged or recurrent acute seizures increase epilepsy risk in a dose-dependent manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".