Resident Use of EEG Cap System to Rule Out Nonconvulsive Status Epilepticus
Bibliographic record
Abstract
BACKGROUND: Nonconvulsive status epilepticus (NCSE) requires an EEG for diagnosis and in many centers access may be limited. The authors aimed to test whether neurology residents can be trained to use and interpret full-montage EEGs using an EEG cap electrode system to detect NCSE while on-call. METHODS: Neurology residents were trained to interpret EEG recordings using the American Clinical Neurophysiology Society critical care EEG terminology. Residents who achieved a score of 70% or higher in the American Clinical Neurophysiology Society certification test and attended a training session were eligible to use the EEG cap on-call with patients suspected of having NCSE. Residents' experience and interpretation of observed EEG patterns were evaluated using a questionnaire. Each EEG recording was independently reviewed by three epilepsy specialists to determine the interpretability of each study and whether the residents correctly identified the EEG patterns. RESULTS: Sixteen residents undertook the training and 12 (75%) achieved a score of 70% or higher on the certification test. Seven of these residents performed 14 EEG cap studies between August 2017 and May 2018. The percent agreement between residents and electroencephalographers was 78.6% for EEG interpretability and 57.1% for description of EEG pattern. Residents did not miss any malignant patterns concerning for NCSE, which accounted for 1 of 14 EEGs but "overcalled" patterns as malignant in 3 of 14 recordings. CONCLUSIONS: This study suggests that neurology residents can be taught to perform and interpret EEGs using a cap system to monitor for NCSE. Additional training will help improve EEG interpretation and sensitivity.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".