Cortical Resection with Electrocorticography for Intractable Porencephaly‐related Partial Epilepsy
Bibliographic record
Abstract
PURPOSE: We evaluated the results of cortical resection of epileptogenic tissue for treatment of intractable porencephaly-related epilepsy. METHODS: We examined clinical features, electrophysiological data, surgical findings, and seizure outcomes after cortical resection in eight patients with intractable epilepsy related to porencephalic cysts. RESULTS: All eight patients had hemiparesis. Five retained motor function in the hemiparetic extremities; six retained visual fields. All had partial seizures, six with secondary generalization. Seven patients had simple and three had complex partial seizures (CPSs); two also had drop attacks. Four patients had multiple seizure types. Long-term scalp video-EEG (LVEEG) localized interictal epileptic abnormalities that anatomically corresponded to the cyst location in three patients. LVEEG recorded ictal-onset zones in five; these anatomically corresponded to the cyst location in three of the five. EEG recorded generalized seizures in two patients, hemispheric in one, and multifocal in two. Intraoperative electrocorticography (ECoG) revealed interictal epileptic areas extending beyond the margins of the cyst in seven patients. We resected ECoG-localized interictal epileptic areas completely in five patients and partially in two. Cortical resection was based on seizure semiology and LVEEG in one patient whose ECoG showed no epileptiform discharges. After a minimum follow-up of 1 year, six patients had excellent seizure outcome (Engel class I), and two had a >90% seizure reduction (Engel class III) without complications. CONCLUSIONS: Cortical resection guided by ECoG allows preservation of motor function and visual field and provides an effective surgical procedure for treatment of intractable epilepsy secondary to porencephaly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".