C.7 Design and implementation of individualized Acute Seizure Action Plans (ASAP): a quality improvement study
Bibliographic record
Abstract
Background: Epilepsy affects approximately 3% of Canadian children. Despite the availability of standardized seizure abortion guidelines, many patients require personalized treatment plans due to genetic factors, medical contraindications, or a history of adverse medication reactions. This study aims to create and evaluate personalized Acute Seizure Action Plans (ASAPs) for epilepsy patients at the Children’s Hospital of Eastern Ontario (CHEO). Methods: Using a Plan-Do-Check-Act (PDCA) framework, we developed electronic ASAPs for integration into participants’ electronic medical records. The effectiveness and user satisfaction of these ASAPs will be evaluated through electronic surveys administered to Neurology physicians, Emergency Department (ED) physicians, and patient participants at baseline and six months post-implementation. Results: Baseline surveys were administered to ED physicians with a 70% response rate, indicating only 43% satisfaction with current generic seizure treatment practice. One hundred percent of respondents expressed interest in using an ASAP, citing challenges in selecting the appropriate anti-seizure medications and determining when to adjust treatment as priorities. These findings underscore the need for ASAP implementation. Conclusions: ED providers desire improved seizure action plans. ASAP implementation is expected to enhance emergency seizure management, reduce adverse events among epilepsy patients, and increase satisfaction of seizure management among all participants.
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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 | 0.055 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".