P.058 Introduction of continuous video EEG monitoring into two different NICU models by training neonatal nurses
Bibliographic record
Abstract
Background: Continuous video-EEG (cvEEG) monitoring is the standard of care for diagnosis and management of neonatal seizures. However, it is labour-intensive. We aimed to establish consistency in monitoring of newborns utilising NICU nurses. Methods: Neonatal nurses were trained to apply scalp electrodes, troubleshoot technical issues. 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. We compared 100 infants with moderate to severe HIE before and after the training program. Results: 192 cvEEGs were performed. Of the 100 infants compared; time to initiate brain monitoring decreased by average of 31.5 hours, in electrographic seizure detection increased(20% compared to 34% a), seizure clinical misdiagnosis decreased (65% compared to 36% ), and Anti-Seizure burden decreased. Conclusions: Training experienced NICU nurses to set-up, start and monitor cvEEG can decrease the time to initiate cvEEG which may lead to better seizure diagnosis and management.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".