Continuous Amplitude-Integrated Electroencephalography During Neonatal and Pediatric Extracorporeal Membrane Oxygenation
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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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".