Healthcare utilization in drug‐resistant epilepsy: A retrospective cohort study into factors predictive of success during short pediatric <scp>EMU</scp> admissions
Bibliographic record
Abstract
OBJECTIVE: The Epilepsy Monitoring Unit (EMU) plays a crucial role in a patient's diagnosis and management of seizures and epilepsy. This is a resource-intensive test; therefore, it is beneficial to be able to predict which patients may need a short admission and which patients may require repeated admissions. The duration of stay required to obtain adequate information is not clear, especially in the pediatric population. In this study, we examine factors that predict success during a short admission and those that predict the need for repeat admissions. METHODS: Retrospective review of 462 admissions (2014-2024). We included any patient admitted to CHEO's EMU for four or fewer days. RESULTS: The median (IQR) admission was 2 (1, 2) days. 23.6% (95% CI: 19.9%, 27.7%) of EMU visits were repeat admissions. 82.0% (95% CI: 78.3%, 85.3%) of admissions were successful. A diagnosis of drug-resistant epilepsy is associated with a higher chance of achieving admission goals (OR = 2.2, 95% CI 1.3, 3.7, p = 0.002). Through a binary logistic regression, we show that a previous diagnosis of drug-resistant epilepsy increases the chance of repeat admission to the EMU (odds ratio = 4.2, 95% CI 2.4, 7.6, p < 0.001), when adjusting for seizure type, admission goals, weaning of meds, age, and gender. Seizure type (focal, generalized, or both) has no influence on the likelihood of repeat admission or achieving admission goals. SIGNIFICANCE: Having a pediatric EMU monitoring period of 1-4 days was sufficient to achieve admission goals in over 80% of patients in this cohort, suggesting that many pediatric patients can have their EMU goals achieve during short stays. Patients with drug-resistant epilepsy are more likely to have a successful initial admission, but also to require repeat admissions. These results can be used to better plan for resource utilization in a pediatric EMU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".