EFFICACY OF EMERGENT ELECTROENCEPHALOGRAPHY (EMEEG) IN DETECTING NONCONVULSIVE SEIZURES.
Bibliographic record
Abstract
Introduction: Identification of non-convulsive seizures is important in neuro critical care practice. Emergent basis Electroencephalography (EmEEG) may helpful in detecting non-convulsive seizures and its medical management. Objective: To assess the yield of EmEEG in detecting non-convulsive seizures. Methods: Study was conducted in a tertiary level super specialty hospital. All patients entered in the emergent EEG register from June 2012 to December 2016 were included. 32 channels Digital EEG (Natus neurology, Canada) was used to perform EEG. Electrodes were placed according to 10-20 system. Clinical history, provisional diagnosis and other lab reports were analyzed. Results: A total of 400 EEGs were analyzed. 40(10%) patients showed periodic complexes, 33(8.3%) patients showed non convulsive seizures, 20(5%)patients showed non-convulsive status epilepticus, 13(3.3%) patients showed complex partial seizures, 4 (1%) patients showed statusepilepticus and 38(9.5%) patients showed inter ictalepileptiform abnormalities. On the whole, out of 400 patients; 53 (13.25%) showed non-convulsive seizures. Conclusion:Emergent EEG has a major role in detecting non-convulsive seizures and neuro-crtical care management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".