Frequency and Predictors of Nonconvulsive Seizures During Continuous Electroencephalographic Monitoring in Critically Ill Children
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence, predictors, and timing of nonconvulsive seizures (NCSz) during continuous electroencephalographic monitoring (cEEG) in critically ill children. METHODS: We identified critically ill children who underwent cEEG during a 4-year period. Multivariate logistic regression analysis was performed to determine variables associated with NCSz. RESULTS: Among 117 monitored children, 44% had seizures on cEEG and 39% had NCSz. The majority of patients with seizures (75%) had purely NCSz, and 23% of patients had status epilepticus, which was purely nonconvulsive in 89% of cases. Seizures occurred immediately on cEEG initiation in 15%, within 1 hour in 50%, and within 24 hours in 80%. Those with clinical seizures prior to cEEG were more likely to have NCSz on cEEG (83%) than those without prior seizures (17%). On multivariate analysis, NCSz were associated with periodic lateralized epileptiform discharges and absence of background reactivity. CONCLUSIONS: Seizures, the majority being NCSz, are common during cEEG in critically ill children (seen in 44% of patients). Half are detected in the first hour of recording, whereas 20% are not detected until after more than 24 hours of recording. Nonconvulsive seizures are associated with periodic lateralized epileptiform discharges and absence of reactivity on cEEG. This study confirms the importance of prolonged cEEG for critically ill children as a means to detect NCSz.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".