GP.04 Prevalence and determinants of seizure action plans in a pediatric epilepsy population
Bibliographic record
Abstract
Background: Status epilepticus (SE) is the most common pediatric neurological emergency. Timely treatment is crucial, yet administration of rescue medications is often delayed and under-dosed. We aim to improve SE management by ensuring that every child at risk of SE in our province has an individualized seizure action plan (SAP) outlining the steps that should be taken during SE. Methods: A survey was distributed to parents of epilepsy patients aged 1 month to 19 years. Primary outcome was percentage of patients with SAPs. Secondary outcome was parental interest in a SAP mobile application. Following chart review, univariate and multivariate analysis was performed to identify variables that predict whether patients have SAPs. Results: Of 192 participants, 61.5% have SAPs. On univariate analysis, history of prior SE and male gender increased likelihood of having a SAP. On logistic regression, Nagelkerke R2 was 0.204 and our model correctly predicted 82.2% of patients with SAPs. 83.3% of parents were interested in a SAP mobile application. Conclusions: This is one of the first studies to examine SAP prevalence in a pediatric epilepsy population. There is a need to increase the percentage of epilepsy patients with SAPs. Most parents would find a SAP mobile application valuable in their child’s management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".