Children with post-stroke epilepsy have poorer outcomes one year after stroke
Bibliographic record
Abstract
Background Epilepsy is a common complication of pediatric stroke. Aim In this study, we aim to measure the association between epilepsy and neurologic outcome after childhood arterial ischemic stroke. Methods Prospective cohort study of children (29 days-19 years) enrolled after an acute arterial ischemic stroke at 21 international pediatric stroke centers and followed to identify epilepsy. One year post-stroke, outcomes were scored using the examination-based Pediatric Stroke Outcome Measure (range = 0-10); higher values reflect greater disability. Ordinal logistic regression was used to measure the association of Pediatric Stroke Outcome Measure scores (categorized as 0-1, 1.5-3, 3.5-6, 6.5-10) with epilepsy. Results Investigators enrolled 86 children (median age = 6.1 years, interquartile range (IQR) = 1.4-12.2 years) with acute stroke. At 1 year, 18/80 (23%) remained on an anticonvulsant including 8/80 (10%) with epilepsy. Among the 70 with Pediatric Stroke Outcome Measure scored, the median was 0.5 (IQR = 0-1.5) for children without epilepsy ( n = 63), and 6 (IQR = 0.5-10) for children with epilepsy ( n = 7). In univariable analyses, poorer 1-year outcome was associated with middle cerebral artery stroke, cortical infarcts, hemorrhagic transformation, hospital disposition not to home, and epilepsy. In multivariable analysis, middle cerebral artery stroke (odds ratio (OR) = 4.9, 95% confidence intervals (CI) = 1.1-21.3) and epilepsy (OR = 24.1, CI = 1.5-380) remained associated with poorer outcome. Conclusions Children who developed epilepsy during the first year post-stroke had poorer neurologic outcomes than those without epilepsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".