Implementation of amplitude-integrated electroencephalography in tertiary Canadian Neonatal Intensive Care Units—a longitudinal study
Bibliographic record
Abstract
AIM: To investigate the implementation of amplitude-integrated electroencephalography (aEEG) as bedside monitoring tool of cerebral function in tertiary Canadian Neonatal Intensive Care Units (NICU) over the past decade. METHODS: Longitudinal study consisting of online surveys of neonatologists on the use of aEEG in 2009 and 2018. RESULTS: The response rate to the survey was 72 of 149 (49%) in 2009 and 18 of 30 (60%) in 2018, respectively. aEEG has been implemented in almost all (2009: 62.5%; 2018: 94%) tertiary Canadian NICUs. Two-thirds (2009: 67%; 2018: 71%) of the respondents considered information from aEEG tracing helpful in clinical practice. The main indications for aEEG were term neonates with hypoxic ischemic encephalopathy (2009 and 2018: 76%) and seizure detection/surveillance (2009: 88%; 2018: 94%). Teaching on aEEG has been implemented for neonatologists (2018: 100%) and health care providers (2018: 50%) in tertiary Canadian NICUs but there is a lack of standardization of training. Use of aEEG in preterm neonates (2009: 37%, 2018: 33%) and application of aEEG in research (18% reported occasional use) is less common. CONCLUSION: aEEG is well established in tertiary Canadian NICUs to monitor cerebral function and detect seizure activity. There is a need to develop formalized aEEG training programs and methods to assess competence. Further implementation of aEEG in preterm neonates and research is desirable.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".