Continuous Amplitude-Integrated Electroencephalography During Neonatal and Pediatric Extracorporeal Membrane Oxygenation
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Bibliographic record
Abstract
PURPOSE: Early prognostication of neurologic outcome in neonates and children supported with extra-corporeal membrane oxygenation (ECMO) is challenging. Amplitude-integrated EEG (aEEG) offers the advantages of continuous monitoring and 24-hours availability at the bedside for intensive care unit providers. The objective of this study was to describe the early electrophysiological background patterns of neonates and children undergoing ECMO and their association with neurologic outcomes. METHODS: This was a retrospective review of neonates and children undergoing ECMO and monitored with aEEG. Amplitude-integrated EEG was summarized as an aEEG background score determined within the first 24 hours of ECMO and divided in 3-hour periods. Screening for electrical seizures was performed throughout the full ECMO duration. Neurologic outcome was defined by the Pediatric Cerebral Performance Category score at hospital discharge. RESULTS: Seventy-three patients (median age 79 days [8-660], median weight 4.78 kg [3.24-10.02]) were included in the analysis. Thirty-two patients had a favorable neurologic outcome and 41 had an unfavorable neurologic outcome group at hospital discharge. A 24-hour aEEG background score >17 was associated with an unfavorable outcome with a sensitivity of 44%, a specificity of 97%, a positive predictive value of 95%, and a negative predictive value of 57%. In multivariate analysis, 24-hour aEEG background score was associated with unfavorable outcome (hazard ratio, 6.1; p = 0.001; 95% confidence interval, 2.31-16.24). The presence of seizures was not associated with neurologic outcome at hospital discharge. CONCLUSIONS: Continuous aEEG provides accurate neurologic prognostication in neonates and children supported with ECMO. Early aEEG monitoring may help intensive care unit providers to guide clinical care and family counseling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it