E-Learning for Pediatric Emergency Department Staff in Point-of-Care Electroencephalogram Interpretation: Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Status epilepticus (SE) represents a critical pediatric emergency necessitating prompt treatment and monitoring. The diagnosis of nonconvulsive SE and the monitoring of convulsive SE require EEG recordings. The integration of simplified point-of-care EEG (pocEEG) may improve care in pediatric emergency departments (PEDs). Objective. OBJECTIVE: This study aims to assess the efficacy of an electronic EEG self-learning module for improving the interpretation of normal cortical activity, artifacts, and seizure patterns in pocEEG by pediatric emergency medicine (PEM) providers. METHODS: This prospective cohort study was conducted in a tertiary academic PED and primarily targeted senior medical staff (SMS) while also engaging junior medical staff (JMS), and registered nurses (RNs). A novel EEG e-learning module trained participants to identify normal cortical activity, artifacts, and seizure patterns. The study comprised pretest, posttest, and three-month retention assessments to evaluate the EEG Total Score as its primary outcome and basic EEG knowledge and confidence measures as secondary outcomes. Outcomes were analyzed using mixed-effects proportional odds logistic regression models. RESULTS: Of 102 PEM providers invited, 61 individuals participated (25 SMS, 15 JMS, 21 RNs), and 29 finished the three-tiered study. In finishers, the EEG Total Score (max = 12 points), indicative of accurate EEG classification, increased substantially between pretest and posttest from a median of 7 (IQR 5-8) to 10 (IQR 7-11) points, corresponding with an increase in the odds of achieving higher EEG Total Scores at the posttest (OR 24.18, 95% CI 7.398 to 79.043, P value < .001). At the retention test the EEG total score remained elevated, although to a lesser extent (median 8 points [IQR 6-9]). Similar trends were observed in secondary outcomes. CONCLUSIONS: The implementation of an e-learning EEG module improved the ability of PEM providers to interpret EEGs. This study highlights the feasibility of imparting basic EEG skills to non-experts through targeted educational interventions. However, the sustained retention of such skills requires improvement, emphasizing the necessity for ongoing refresher training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".