Non-neurophysiologist Physicians and Nurses Can Detect Subclinical Seizures in Children Using a Panel of Quantitative EEG Trends and a Seizure Detection Algorithm
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Bibliographic record
Abstract
PURPOSE: This study evaluated the sensitivity of nonconvulsive seizure detection by non-neurophysiologist physicians and nurses using a panel of quantitative EEG (QEEG) trends in the setting of a pediatric intensive care unit. METHODS: Forty-five 1-hour QEEG epochs were obtained retrospectively from 10 patients admitted to the McMaster Children's Hospital pediatric intensive care unit, which included 184 electrographic seizures. Each epoch constituted 4 QEEG trends, a seizure probability marker, automated seizure detector, rhythmicity spectrograms, and amplitude-integrated EEG. Six pediatric residents and 5 pediatric intensive care unit nurses analyzed the epochs for possible seizures after a 15-minute power point presentation. This was compared with the gold standard of a board-certified epileptologist interpreting the conventional EEG data for seizures. RESULTS: Sensitivity of seizure detection for pediatric residents and intensive care unit nurses were 0.90. The specificity was 0.87 and 0.89, respectively. The interrater agreement among the pediatric residents was moderate with a kappa (κ) value of 0.45 (confidence interval: 0.41-0.49), and among the nurses were moderate with a κ value of 0.59 (confidence interval: 0.54-0.63). A post hoc analysis involving 2 neurophysiologists demonstrated a sensitivity of 0.90 and a specificity of 0.93 (confidence interval: 0.90-0.96) for seizure detection and a substantial interrater agreement of κ = 0.76 (confidence interval: 0.61-0.91). CONCLUSIONS: A panel of QEEG trends can be used by non-neurophysiologists in a pediatric critical care setting to detect nonconvulsive seizures with a reasonable accuracy, which may expedite subclinical seizure identification and timely intervention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it