Abstract 18: Anticonvulsant Medication Practice Varies After Pediatric Arterial Ischemic Stroke
Bibliographic record
Abstract
Introduction: Acute symptomatic seizures are common after pediatric arterial ischemic stroke (AIS). We sought to analyze practice variation in anticonvulsant medication treatment (ACM) after AIS. We hypothesized that ACM practice varies but is related to age, acute seizure frequency and duration, cortical involvement, and hemorrhagic conversion. Methods: Seizures in Pediatric Stroke (SIPS), an international, prospective study, enrolled neonatal (<28 days) and childhood (<19 years) acute AIS patients 3/2011-8/2012. Seizures and ACM were recorded. Among patients with acute seizures (<7 days post-stroke), we used univariate Spearman’s correlations to determine association between ACM and clinical predictors at both discharge and 12-month time points. Variables with p <0.05 at univariate analysis were entered into a logistic regression model. Results: Among 116 patients, 27 neonates and 31 children (ages 0.2-17 years) had an acute seizure. In neonates, 24 (89%) had ACM continued after discharge, but only 4 (15%) remained on ACM at 12 m (3 having had post-discharge seizure). Phenobarbital was the most common ACM in neonates at discharge (n=14) and 12m (n=2, levetiracetam = 2). In children, 23 (74%) had ACM at discharge, and 17 (57%) remained on ACM at 12m (7 having had a post-discharge seizure). All patients in study with post-discharge seizure were discharged on ACM. Two neonates and 1 child with post-discharge seizure were not on ACM at 12m. Levetiracetam was the most common ACM in children at discharge (n=11) and 12m (n=9). No single variable correlated with discharge ACM (Table). ACM at 12 m correlated with neonatal stroke, >10 seizures in acute period, and post-discharge seizure. In logistic regression, all variables remained significant. Conclusion: ACM management after acute post-stroke seizures varies widely in pediatric stroke. Neonates rarely, but children frequently, are continued on ACM up to 1 year, even without further seizures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".