Magnetoencephalography-guided resection of epileptogenic foci in children
Bibliographic record
Abstract
OBJECT: Resective surgery is increasingly used in the management of pediatric epilepsy. Frequently, invasive monitoring with subdural electrodes is required to adequately map the epileptogenic focus. The risks of invasive monitoring include the need for 2 operations, infection, and CSF leak. The aim of this study was to evaluate the feasibility and outcomes of resective epilepsy surgery guided by magnetoencephalography (MEG) in children who would have otherwise been candidates for electrode implantation. METHODS: The authors reviewed the records of patients undergoing resective epilepsy surgery at the Hospital for Sick Children between 2001 and 2010. They identified cases in which resections were based on MEG data and no intracranial recordings were performed. Each patient's chart was reviewed for presentation, MRI findings, MEG findings, surgical procedure, pathology, and surgical outcome. RESULTS: Sixteen patients qualified for the study. All patients had localized spike clusters on MEG and most had abnormal findings on MRI. Resection was carried out in each case based on the MEG data linked to neuronavigation and supplemented with intraoperative neuromonitoring. Overall, 62.5% of patients were seizure free following surgery, and 20% of patients experienced an improvement in seizures without attaining seizure freedom. In 2 cases, additional surgery was performed subsequently with intracranial monitoring in attempts to obtain seizure control. CONCLUSIONS: MEG is a viable alternative to invasive monitoring with intracranial electrodes for planning of resective surgery in carefully selected pediatric patients with localization-related epilepsy. Good candidates for this approach include patients who have a well-delineated, localized spike cluster on MEG that is concordant with findings of other preoperative evaluations and patients with prior brain pathologies that make the implantation of subdural and depth electrodes somewhat problematic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".