Validating StatNet EEG as a Reliable and Effective Tool in the Diagnosis of Non-Convulsive Status Epilepticus: Phase 2 (P5.011)
Bibliographic record
Abstract
Objective: 1. Demonstrate the validity of the StatNet portable EEG system in diagnosing seizures and Non-convulsive status epilepticus (NCSE) when conventional EEG is unavailable. 2. Develop clinical criteria to identify patients at risk of NCSE. Background: NCSE is clinically subtle and it dramatically increases morbidity and mortality. This makes it difficult to diagnose without EEG. StatNet EEG provides a quick alternative to conventional EEG, which is often unavailable after hours. Methods: Patients are primarily recruited from the Emergency Department. Each patient received a StatNet EEG and a conventional EEG, when available. We blinded and compared the studies, assessing delay between the studies, setup time, artifact, and detection of abnormalities using conventional EEG as controls. The nonparametric Mann-Whitney two-sample T-test was used. Results are expressed in mean minutes +/- standard deviation. Inter-observer reliability was assessed by Kappa score. Results: 19 patients were collected. Two StatNet EEGs were not interpretable and were excluded. Mean age is 60y ±21.94y (17-93y range). 68[percnt] (N=13) are male. The inter-observer agreement for detection of any abnormalities is 0.54 (0.18, CI=0.19-0.89) for StatNet EEG and 0.73 (0.18, CI=0.37-1.0) for conventional EEG. Inter-observer agreement for epileptiform discharges was 0.76 (0.22, CI=0.33-1.0) for StatNet EEG and 0.76 (0.16, CI=0.43-1.0) for conventional EEG. Inter-observer agreement for NCSE was 1.00 (0, CI=1.00-1.00) for StatNet EEG and 0.64 (0.32, CI=0.0031-1.0) for conventional EEG, in the single identified case. Statnet 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 demonstrating that StatNet EEG is a reliable and effective tool, aiding specifically in early recognition and management of NCSE. The potential exists to decrease associated morbidity and mortality. Once StatNet is validated, we will ask for Health Canada approval to implement a stat EEG pathway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".