The Impact of Amplitude-Integrated Electroencephalography on NICU Practice
Bibliographic record
Abstract
OBJECTIVE: To examine how the introduction of amplitude-integrated electroencephalography (aEEG) to our neonatal intensive care unit (NICU) influenced clinical practice. METHODS: This was a retrospective study examining clinical practice three years before and three years after the introduction of aEEG monitors to our NICU. A time series analysis was performed to explore whether aEEG introduction was associated with changes in the rates of conventional EEGs performed, neurology consultations and neonates diagnosed with seizures. RESULTS: Following aEEG introduction, the total number of conventional EEGs performed remained constant; however, there was significant shift in conventional EEG utilization towards neonates receiving fewer multiple EEGs and more single EEGs. There was no change in the rate of neurology consultations or the number of neonates diagnosed with seizures. CONCLUSIONS: Introduction of aEEG monitors to our NICU has led to less reliance on conventional EEG as a tool for the serial evaluation of brain function. Since the number of neonates diagnosed with seizures did not increase, aEEG monitoring did not appear to uncover a significant subgroup of patients with subclinical seizures that would previously have gone undetected. Conventional EEG and aEEG are complementary tools for the assessment of newborn cerebral function.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".