P.069 Is 4 days enough? an investigation into short admissions to the Epilepsy monitoring unit
Bibliographic record
Abstract
Background: The Epilepsy Monitoring Unit (EMU) plays a crucial role in a patient’s diagnosis and management for seizures and epilepsy. The duration of stay required to obtain adequate information is not clear, especially in the pediatric population. In this study, we examine whether a one to four day length of stay in the EMU is sufficient to obtain the necessary information. Methods: Retrospective review of 522 admissions (2014-2021). Included any patient admitted to CHEO’s EMU for any length of time. Results: The average admission was 1.75 days with 35.7% of patients requiring repeat EMU visits. Through a binary logistic regression, we show that a previous diagnosis of refractory seizures increases the chance of readmission to the EMU. However, a diagnosis of refractory seizures is also associated with a higher chance of achieving admission goals. While other factors including seizure type, weaning of meds, goals of admission, age, and gender have no influence on likelihood of readmission or achieving admission goals. Conclusions: This study indicates that having a short admission for EMU monitoring is sufficient to capture enough data to achieve admission goals in the pediatric population.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".