Outcomes of stereoelectroencephalography exploration at an epilepsy surgery center
Bibliographic record
Abstract
OBJECTIVES: Epilepsy surgery is offered in resistant focal epilepsy. Non-invasive investigations like scalp video EEG monitoring (SVEM) help delineate epileptogenic zone. Complex cases may require intracranial video EEG monitoring (IVEM). Stereoelectroencephalography (SEEG)-based intracerebral electrode implantation has better spatial resolution, lower morbidity, better tolerance, and superiority in sampling deep structures. Our objectives were to assess IVEM using SEEG with regard to reasoning behind implantation, course, surgical interventions, and outcomes. MATERIALS AND METHODS: Seventy-two admissions for SEEG from January 2014 to December 2018 were included in the study. Demographic and clinical data were retrospectively collected. RESULTS: The cohort comprised of 69 adults of which 34 (47%) had lesional MRI. Reasons for SEEG considering all cases included non-localizing ictal onset (76%), ictal-interictal discordance (21%), discordant semiology (17%), proximity to eloquent cortex (33%), nuclear imaging discordance (34%), and discordance with neuropsychology (19%). Among lesional cases, additional reasons included SVEM discordance (68%) and dual or multiple pathology (47%). Forty-eight patients (67%) were offered resective surgery, and 41 underwent it. Twenty-three (56%) had at least one year post-surgical follow-up of which 14 (61%) had Engels class I outcome. Of the remaining 23 who were continued on medical management, 4 (17%) became seizure-free and 12 (51%) had reduction in seizure frequency. CONCLUSION: SEEG monitoring is an important and safe tool for presurgical evaluation with good surgical and non-surgical outcomes. Whether seizure freedom following non-surgical management could be related to SEEG implantation, medication change, or natural course needs to be determined.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".