Introduction of Continuous Video EEG Monitoring into 2 Different NICU Models by Training Neonatal Nurses
Bibliographic record
Abstract
BACKGROUND: Continuous video electroencephalographic (EEG) (cvEEG) monitoring is emerging as the standard of care for diagnosis and management of neonatal seizures. However, cvEEG is labor-intensive and the need to initiate and interpret studies on a 24-hour basis is a major limitation. PURPOSE: This study aims at establishing consistency in monitoring of newborns admitted to 2 different neonatal intensive care units (NICUs) managed by the same neurocritical care team. METHODS: Neonatal nurses were trained to apply scalp electrodes, troubleshoot technical issues, and identify amplitude-integrated EEG abnormalities. Guidelines, checklists, and visual training modules were developed. A central network system allowed remote access to the cvEEGs by the epileptologist for timely interpretation and feedback. A cohort of 100 infants with moderate to severe hypoxic-ischemic encephalopathy before and after the training program was compared. RESULTS: During the study period, 192 cvEEGs were obtained. The time to initiate brain monitoring decreased by 31.5 hours posttraining; this, in turn, led to an increase in electrographic seizure detection (20% before vs 34% after), decrease in seizure clinical misdiagnosis (65% before and 36% after), and reduction in antiseizure medication burden. IMPLICATIONS FOR PRACTICE: Training experienced NICU nurses to set up, start, and monitor cvEEGs can decrease the time to initiate cvEEGs, which may lead to better seizure diagnosis and management. IMPLICATIONS FOR RESEARCH: Further understanding of practice bundles for best supporting infants at risk and being treated for seizures needs to be evaluated for integration into practice.Video Abstract Available at https://journals.lww.com/advancesinneonatalcare/Pages/videogallery.aspx.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".