Predictors of nonconvulsive seizures among critically ill children
Bibliographic record
Abstract
PURPOSE: Continuous electroencephalography (EEG) monitoring is a valuable tool for the detection of seizures among critically ill children, in particular when these seizures occur without clinical signs: termed nonconvulsive seizures. Continuous EEG monitoring is a limited resource in many centers. We sought to identify which critically ill children most frequently experience nonconvulsive seizures, and thus may particularly benefit from continuous EEG monitoring. METHODS: Single-center review was undertaken of consecutive diagnostic continuous EEG (cEEG) recordings performed in our pediatric and neonatal intensive care units (ICUs). We examined the indications for monitoring, the clinical characteristics of monitored patients, the occurrence and timing of seizures, and clinical and EEG characteristics associated with nonconvulsive seizures. KEY FINDINGS: One hundred twenty-one patients underwent diagnostic continuous EEG monitoring, for a mean duration of 26 h. Seizures were detected in 32% of these patients, of which 90% experienced some nonconvulsive seizures, and 72% experienced exclusively nonconvulsive seizures. Patients with nonconvulsive seizures had significantly greater odds of having acute epilepsy, acute structural brain injury, prior in-hospital convulsive seizures, and the presence of interictal epileptiform abnormalities on EEG. SIGNIFICANCE: Seizures are common among critically ill children undergoing diagnostic cEEG monitoring. The great majority of these seizures are nonconvulsive, requiring EEG for their detection. Predictors of nonconvulsive seizures include acute epilepsy, acute structural brain injury, prior in-hospital convulsive seizures, and interictal epileptiform abnormalities on EEG. These findings can help inform future allocation of limited cEEG monitoring resources to those patients at greatest risk for nonconvulsive seizures.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".