D.04 Validating StatNet EEG as a reliable and effective tool in the Diagnosis of Non-Convulsive Status Epilepticus after hours
Bibliographic record
Abstract
Background: NCSE dramatically increases morbidity and mortality. It is clinically subtle, which makes it difficult to diagnose without EEG. StatNet EEG provides a quick alternative to conventional EEG, which is often unavailable after hours. Methods: Each patient received a StatNet EEG by a neurology resident and a conventional EEG by an EEG technologist. Blinded studies were compared for delay between the studies, setup time, artifact, and abnormality detection using conventional EEG as controls. Nonparametric Mann-Whitney two-sample T-test was used. Results are expressed in mean minutes +/- SD. Kappa score assessed inter-observer reliability. Results: 19 patients were collected. Two StatNet EEGs were not interpretable and excluded. The mean delay between studies was 26h53min. Electrode placement is significantly shorter: 13:14±5:24 StatNet EEG vs 18:07±5:35 conventional EEG (p=0.02). Conclusions: There is high inter-rater reliability between the conventional and StatNet EEG groups (table 1) demonstrating that StatNet is a reliable and effective tool, aiding in early recognition and management of NCSE.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".