Prevalence, Risk Factors, and Impact of Preoperative Seizures in Neonates With Congenital Heart Disease
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess the prevalence, risk factors, and impact of electrographic seizures in neonates with complex congenital heart disease before cardiac surgery. METHODS: A cohort of 31 neonates with congenital heart disease monitored preoperatively with continuous video-EEG (cEEG) was first reviewed for electrographic seizure burden and EEG background abnormalities. Second, cEEG findings were correlated with brain MRI and 18-month outcomes. RESULTS: Continuous video-EEG was recorded preoperatively for a median duration of 20.5 hours (range, 2.5-93.5 hours). The five neonates (16%; 95% confidence interval, 5.5% to 34%) with seizures detected on cEEG in the preoperative period had a diagnosis of transposition of the great arteries or similar physiology, detected in four of five postnatally. None of the 157 recorded electrographic seizures had a clinical correlate. The median time to first seizure was 65 minutes (range, 6-300 minutes) after cEEG hookup. The median maximum hourly seizure burden was 12.4 minutes (range, 7-23 minutes). Before the first electrographic seizure, a prolonged interburst interval (>10 seconds) was not associated with seizures (coefficient 1.2; 95% confidence interval, -1.1 to 3.6). MRI brain lesions were three times more common in neonates with seizures. Sharp wave transients on cEEG were associated with delayed opercular development. CONCLUSIONS: In this cohort, preoperative electrographic seizures were common, were all subclinical, and were associated with MRI brain injury and postnatal diagnosis of transposition of the great arteries. The findings motivate further study of the mechanisms of preoperative brain injury, particularly among neonates with a postnatal diagnosis of transposition of the great arteries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".