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 nonconvulsive seizures and neuro-crtical care management.
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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 | 0.007 |
| 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) | 0.001 | 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 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".