B.03 Clinical utility of critical care EEG monitoring in a Canadian paediatric centre
Bibliographic record
Abstract
Background: Continuous electroencephalographic (cEEG) monitoring is essential to diagnosing non-convulsive seizures (NCS), reported to occur in 7-46% of at-risk critically ill patients. However, cEEG is labour-intensive, and given scarcity of resources at most centres cEEG is feasible in only selected patients. We aim to evaluate the clinical utility of cEEG at our centre in order to optimize further cEEG allocation among critically ill patients. Methods: Using a clinical database, we identified critically ill children who underwent cEEG monitoring in 2016, 2017 and 2018. We abstracted underlying diagnoses, indication for cEEG monitoring, cEEG findings, and associated changes in management. Results: Over this three year period, 928 cEEGs were performed. Among the 100 studies analyzed to date, primary indications for monitoring were characterization of events of unclear etiology (32%), diagnosis of NCS (30%), and monitoring of therapy for seizures (17%). Seizures were captured in 31% of patients (22% subclinical only, 5% electroclinical only, 4% both), which resulted in a treatment change in 90% of cases. Non-epileptic events were captured in 26% of patients. Conclusions: cEEG yielded clinically meaningful information in 57% of cases, frequently resulting in management changes. Subgroup analyses by cEEG indication and ICU location will be presented.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".