Safety and Efficacy of Stereoelectroencephalography in Pediatric Epilepsy Surgery
Bibliographic record
Abstract
Abstract Stereoelectroencephalography (SEEG) is the preoperative assessment of choice when the epileptogenic zone (EZ) is unclear in patients requiring surgery for severe, drug-refractory epilepsy. There are relatively little data on the safety and efficacy of SEEG in the pediatric epilepsy population. We, therefore, investigated the insertional complications, rate of successful identification of the EZ, and long-term seizure outcomes following surgery after SEEG in children. This was a retrospective study of drug-resistant pediatric epilepsy patients treated with surgery between 2005 and 2020 and who underwent presurgical SEEG. Rationale for and coverage of SEEG, identification of the EZ, and ultimate seizure outcome following SEEG-tailored resections were collected and analyzed. Thirty patients (15 male, mean age: 12.4 ± 5 years) who underwent SEEG were studied. SEEG-related complications occurred in one case (3%). A total of 190 multicontact electrodes (mean: 7.0 ± 2.5 per patient) were implanted across 30 insertions capturing 440 electrographic seizures (mean: 17.5 ± 27.6 per patient). The most common rationale for SEEG was normal magnetic resonance imaging with surface EEG that failed to identify the EZ (17/30; 57%). SEEG identified a putative EZ in all cases, resulting in SEEG-tailored resections in 25/30 (83%). Freedom from disabling seizures was achieved following resections in 20/25 cases (80%) with 5.9 ± 4.0 years of postoperative follow-up. SEEG is a safe and effective way to identify the EZ in the presurgical evaluation of children with refractory epilepsy and permits effective and long-lasting SEEG-tailored resections.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".