Benign EEG Patterns: Is there More to Learn?
Why this work is in the frame
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Bibliographic record
Abstract
Prevalence of Benign Epileptiform Variants. Santoshkumar B, Chong JJ, Blume WT, McLachlan RS, Young GB, Diosy DC, Burneo JG, Mirsattari SM. Clin Neurophysiol 2009;120(5):856–861. OBJECTIVE: There are numerous distinctive benign electroencephalographic (EEG) patterns which are morphologically epileptiform but are non-epileptic. The aim of this study was to determine the prevalence of different benign epileptiform variants (BEVs) among subjects who underwent routine EEG recordings in a large EEG laboratory over 35 years. METHODS: We retrospectively studied the prevalence of BEVs among 35,249 individuals who underwent outpatient EEG recordings at London Health Sciences Centre in London, Ontario, Canada between January 1, 1972 and December 31, 2007. The definitions of the Committee on Terminology of the International Federation of Societies for EEG and Clinical Neurophysiology (IFSECN) were used to delineate epileptiform patterns (Chatrian et al. A glossary of terms most commonly used by clinical electroencephlographers. Electroenceph Clin Neurophysiol 1974;37:538–48) and the descriptions of Klass and Westmoreland [Klass DW, Westmoreland BF. Nonepileptogenic epileptiform electroenephalographic activity. Ann Neurol 1985;18:627–35] were used to categorize the BEVs. RESULTS: BEVs were identified in 1183 out of 35,249 subjects (3.4%). The distribution of individual BEVs were as follows: benign sporadic sleep spikes 1.85%, wicket waves 0.03%, 14 and 6 Hz positive spikes 0.52%, 6 Hz spike-and-waves 1.02%, rhythmic temporal theta bursts of drowsiness 0.12%, and subclinical rhythmic electrographic discharge of adults in 0.07%. CONCLUSION: The prevalence of six types of BEVs was relatively low among the Canadian subjects when compared to the reports from other countries.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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 it