Continuous EEG Monitoring in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Continuous EEG monitoring provides an opportunity to both accurately identify seizures and monitor the neurologic status of critically ill neonates in the intensive care unit. The incidence of seizures is higher in the neonatal period than at any other time in life. Seizures and abnormalities of EEG background are associated with significant risk of mortality and long-term neurodevelopmental morbidities. In the neonatal population the majority of seizures are not clinically evident and go undetected without EEG monitoring. We review the incidence and risk factors for neonatal seizures, and the utility of continuous EEG monitoring in the neonatal intensive care unit for seizure detection and for analysis of background to allow prognostication. We consider the role of amplitude-integrated EEG in the neonatal population. We consider the utility of continuous EEG for frequently encountered neurologic indications and discuss the outcome data and some new developments in continuous EEG monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".