A multi-modality approach to identifying primary generalized epilepsy that can mimic focal epilepsy
Bibliographic record
Abstract
Introduction: Evaluating the suitability for surgery in patients with epilepsy requires determining if the epilepsy is focal or generalized. Presurgical workups can indicate focal epilepsy in certain cases of generalized epilepsy (GE). The purpose of this study was to identify distinctive features which characterize patients with primary GE that mimics focal epilepsy. Method: We retrospectively identified 19 children with generalized interictal discharges during scalp video-EEG (SVEEG) and underwent invasive monitoring and/or epilepsy surgery. Two children did not undergo resective surgery due to final diagnosis of primary GE (Group A). Seventeen children underwent a resective surgery (Group B). Scalp video-EEG, MEG, MRI, and intracranial video EEG (IVEEG) were reviewed. Results: On (SVEEG), the frequency of generalized spike-and-waves (GSW) was 3Hz in Group A and 1.5-2.5Hz in Group B. Group A had only absence seizures , whereas 80% in Group B had multiple types of seizures. Both groups had lateralized MEG dipoles. One patient in Group A had a focal MRI abnormality. In Group A, IVEEG showed GSW of 3 Hz frequency with inconsistent leading. In Group B, IVEEG showed consistent localization of ictal and interictal high frequency oscillations. Conclusion: Children with generalized 3 Hz spike-and-waves and only absence seizures may be a contraindication of resective surgery even though some presurgical workup shows focality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".