P.056 Combined conventional and amplitude-integrated EEG monitoring in neonates: a prospective study
Bibliographic record
Abstract
Background: Seizure monitoring via amplitude-integrated EEG (aEEG) is standard of care in many NICUs; however, conventional EEG (cEEG) is the gold standard for seizure detection. We compared the diagnostic yield of aEEG interpreted at the bedside, aEEG interpreted by an expert, and cEEG. Methods: Neonates received aEEG and cEEG in parallel. Clinical events and aEEG were interpreted at bedside and subsequently independently analyzed by experienced neonatology and neurology readers. Sensitivity and specificity of bedside aEEG as compared to expert aEEG interpretation and cEEG were evaluated. Results: Thirteen neonates were monitored for an average duration of 33 hours (range 15-94). Fourteen seizure-like events were detected by clinical observation, and 12 others by bedside aEEG analysis. None of the bedside aEEG events were confirmed as seizures on cEEG. Expert aEEG interpretation had a sensitivity of 13% with 46% specificity for individual seizure detection (not adjusting for patient differences), and a sensitivity of 50% with 46% specificity for detecting patients with seizures. Conclusions: Real-world bedside aEEG monitoring failed to detect seizures evidenced via cEEG, while misclassifying other events as seizures. Even post-hoc expert aEEG interpretation provided limited sensitivity and specificity. Considering the poor sensitivity and specificity of bedside aEEG interpretation, combined monitoring may provide limited clinical benefit.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".