Identifying the Primary Epileptogenic Hemisphere from Electroencephalographic (EEG) and Magnetoencephalographic Dipole Lateralizations in Children With Intractable Epilepsy
Bibliographic record
Abstract
We used electroencephalographic (EEG) and magnetoencephalographic dipole lateralizations to identify the primary epileptogenic hemisphere in 41 children with intractable localization-related epilepsy. We compared EEG and magnetoencephalographic dipole lateralizations, EEG ictal onsets, and magnetic resonance images (MRIs). Concordant lateralization of EEG and magnetoencephalographic dipoles (> 50% of each lateralizing to the same hemisphere) occurred in 34 patients, with EEG ictal onsets in the same hemisphere in 23 (68%) and concordant MRI lesions in 23 (68%). Focal resection in 16 of 20 patients resulted in a good surgical outcome. Of the seven children with nonconcordant magnetoencephalographic and EEG lateralizations, one (14%) had EEG ictal onset and one (14%) had MRI lesions that lateralized; none had surgery. The relationship between lateralized EEG and magnetoencephalographic dipoles forecasts surgical candidacy. Concordant lateralizations predict good seizure control after surgery by identifying the primary epileptogenic hemisphere. Discordant lateralizations signify an undetermined epileptogenic hemisphere and contraindicate surgery without further testing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".