Stereoelectroencephalography and surgical outcome in polymicrogyria‐related epilepsy: A multicentric study
Bibliographic record
Abstract
OBJECTIVE: We aimed to (1) assess the concordance between various polymicrogyria (PMG) types and the associated epileptogenic zone (EZ), as defined by stereoelectroencephalography (SEEG), and (2) determine the postsurgical seizure outcome in PMG-related drug-resistant epilepsy. METHODS: We retrospectively analyzed 58 cases: 49 had SEEG and 39 corticectomy or hemispherotomy. RESULTS: Mean age at SEEG or surgery was 28.3 years (range, 2-50). PMG was bilateral in 9 (16%) patients and unilateral in 49, including 17 (29%) unilobar, 12 (21%) multilobar, 15 (26%) perisylvian, and only 5 (9%) hemispheric. Twenty-eight (48%) patients additionally had schizencephaly, heterotopia, or focal cortical dysplasia. The SEEG-determined EZ was fully concordant with the PMG in only 8 (16%) cases, partially concordant in 74%, and discordant in 10%. The EZ included remote cortical areas in 21 (43%) cases and was primarily localized in those in 5 (10%), all related to the mesial temporal structures. All but 1 PMG patient with corticectomy or hemispherotomy had a unilateral PMG. At last follow-up (mean, 4.6 years; range, 1-16), 28 (72%) patients remained seizure free. Shorter epilepsy duration to surgery was an independent predictor of seizure freedom. INTERPRETATION: PMG-related drug-resistant epilepsy warrants a comprehensive presurgical evaluation, including SEEG investigations in most cases, given that the EZ may only partially overlap with the PMG or include solely remote cortical areas. Seizure freedom is feasible in a large proportion of patients. PMG extent should not deter from exploring the possibility of epilepsy surgery. Our data support the early consideration of epilepsy surgery in this patient group. Ann Neurol 2017;82:781-794.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".