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 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".