Electroencephalography monitoring in critically ill children: Current practice and implications for future study design
Bibliographic record
Abstract
PURPOSE: Survey data indicate that continuous electroencephalography (EEG) (CEEG) monitoring is used with increasing frequency to identify electrographic seizures in critically ill children, but studies of current CEEG practice have not been conducted. We aimed to describe the clinical utilization of CEEG in critically ill children at tertiary care hospitals with a particular focus on variables essential for designing feasible prospective multicenter studies evaluating the impact of electrographic seizures on outcome. METHODS: Eleven North American centers retrospectively enrolled 550 consecutive critically ill children who underwent CEEG. We collected data regarding subject characteristics, CEEG indications, and CEEG findings. KEY FINDINGS: CEEG indications were encephalopathy with possible seizures in 67% of subjects, event characterization in 38% of subjects, and management of refractory status epilepticus in 11% of subjects. CEEG was initiated outside routine work hours in 47% of subjects. CEEG duration was <12 h in 16%, 12-24 h in 34%, and >24 h in 48%. Substantial variability existed among sites in CEEG indications and neurologic diagnoses, yet within each acute neurologic diagnosis category a similar proportion of subjects at each site had electrographic seizures. Electrographic seizure characteristics including distribution and duration varied across sites and neurologic diagnoses. SIGNIFICANCE: These data provide a systematic assessment of recent CEEG use in critically ill children and indicate variability in practice. The results suggest that multicenter studies are feasible if CEEG monitoring pathways can be standardized. However, the data also indicate that electrographic seizure variability must be considered when designing studies that address the impact of electrographic seizures on outcome.
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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 | 0.112 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".