The prevalence of seizures in comatose children in the pediatric intensive care unit: A prospective video‐EEG study
Bibliographic record
Abstract
PURPOSE: Studies in adult and neonatal intensive care units (ICUs) report a high prevalence of epileptic seizures in comatose patients. The prevalence of seizures in pediatric ICUs is variably reported in a few retrospective studies using different electroencephalography (EEG) methods. We aimed to determine prospectively the prevalence of epileptic seizures (clinical and subclinical) in comatose children in the pediatric ICU using continuous video-EEG (v-EEG) monitoring. METHODS: We performed v-EEG in consecutive children aged 2 months to 17 years admitted to the pediatric ICU with sustained depressed consciousness over a period of 15 months. RESULTS: We monitored 100 comatose children, 69% within 24 h of ICU admission. Median length of ICU stay was 5 days. Median duration of v-EEG was 20 h. Epileptic seizures were identified in only seven patients, of whom six had a history of epilepsy with witnessed seizures immediately prior to v-EEG. All epileptic seizures were recorded in the first 3 h of v-EEG. Seizures were suspected by ICU staff in 18 monitored patients, only four of whom had confirmed epileptic seizures. DISCUSSION: The lower prevalence of epileptic seizures and the shorter length of ICU stay in children compared to adults and neonates suggest a different spectrum of disease and neurologic response. Short-duration v-EEG in patients with a history of prior seizures, epilepsy, or clinical events suspected to be seizures seems more appropriate than routine v-EEG in all comatose children in the pediatric ICU.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".