P.130 Canadian physician attitudes towards long term EEG monitoring in the neonatal intensive care unit
Bibliographic record
Abstract
Background: Long-term EEG monitoring (LTEM), including amplitude-integrated (aEEG) or conventional EEG (cEEG), is increasingly being used in critically ill neonates. Despite an abundance of studies regarding the clinical utility of LTEM, much is unknown regarding provider attitudes toward this tool. We aimed to evaluate neurologist and neonatologist opinions regarding LTEM in the NICU and describe current Canadian practices. Methods: A 15-item questionnaire was developed with input from neonatologists and pediatric neurologists at two Canadian centres. The questionnaire was piloted at our hospital and subsequently distributed to Canadian neonatologists and pediatric neurologists. Results: All 16 local respondents use LTEM in the NICU. Neonatologists were more likely to combine aEEG and cEEG, and monitor for longer durations than pediatric neurologists. However, most pediatric neurologists would like to monitor more (71%), compared to neonatologists who were more likely to say that current monitoring practices are sufficient. High rates of neonatologists (88%) and neurologists (85%) are interested in attending an education session on LTEM. Conclusions: Preliminary data suggests neonatologists and pediatric neurologists differ in their approach to LTEM. Results from our national questionnaire will be analyzed shortly, and may inform the development of educational materials as well as future studies that involve multi-centre efforts.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.016 | 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".