Utility of Magnetoencephalography in the Evaluation of Recurrent Seizures after Epilepsy Surgery
Bibliographic record
Abstract
PURPOSE: To study the role of magnetoencephalography (MEG) in the surgical evaluation of children with recurrent seizures after epilepsy surgery. METHODS: We studied 17 children with recurrent seizures after epilepsy surgery using interictal and ictal scalp EEG, intracranial video EEG (IVEEG), MRI, and MEG. We analyzed the location and distribution of MEG spike sources (MEGSSs) and the relationship of MEGSSs to the margins of previous resections and surgical outcome. RESULTS: Clustered MEGSSs occurred at the margins of previous resections within two contiguous gyri in 10 patients (group A), extended spatially from a margin by < or =3 cm in three patients (group B), and were remote from a resection margin by >3 cm in six patients (group C). Two patients had concomitant group A and C clusters. Thirteen patients underwent second surgeries. IVEEG was used in four patients. Six of seven patients with group A MEGSS clusters did not require IVEEG for second surgeries. Follow-up periods ranged from 0.6 to 4.3 years (mean: 2.6 years). Eleven children, including eight who became seizure-free, achieved Engel class I or II. CONCLUSION: Our data demonstrate the utility of MEG for evaluating patients with recurrent seizures after epilepsy surgery. Specific MEGSS cluster patterns delineate epileptogenic zones. Removing cluster regions adjacent to the margins of previous resections, in addition to removing recurrent lesions, achieves favorable surgical outcome. Cluster location and extent identify which patients require IVEEG, potentially eliminating IVEEG for some. Patients with remotely located clusters require IVEEG for accurate assessment and localization of the entire epileptogenic zone.
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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 | 0.006 | 0.001 |
| 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 | 0.000 | 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 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".