P.037 Role of epilepsy monitoring unit in the investigation of patients with epilepsy and developmental delay
Bibliographic record
Abstract
Background: A significant part of the developmental delay (DD) population has epilepsy (26-70%) and live in an institution. These patients tend to have atypical presentation of epileptic seizures with higher risk of misdiagnosis. Distinguishing their ictal events from paroxysmal behaviors can be challenging.There often is a lack of description of the spells or inadequate history from the caregivers or the patients. These patients often have drug resistant epilepsy requiring polypharmacy with increased risk of morbidity and mortality. The aim of this study was to determine usefulness of Epilepsy Monitoring Unit (EMU) in diagnosis and management of these patients. Methods: This is a retrospective observational study of the patients with epilepsy and DD living in institutions that were admitted to the EMU. Results: Four patients met the inclusion criteria for this study. The mean age was 45(29-71), 3/4 (N=3) were male and 3/4 had focal epilepsy. All patients had mood disorders and 2 were taking antipsychotic medication. The mean admission-time was 6,25 days (2-15) and there was a correlation with the events and seizures in 2/4 of the patients and the rest had a combination of behavioural-changes and seizures. Conclusions: EMU admission can provide an accurate diagnosis of spells in patients with DD and epilepsy, and improve their quality of life.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".