P.096 Effectiveness of palliative focal resective surgery in intracranial EEG confirmed multifocal intractable epilepsy in adult patients
Bibliographic record
Abstract
Background: Effectiveness of “palliative resections” of a dominant epileptogenic focus in adults with multifocal intractable epilepsy confirmed on intracranial EEG has rarely been reported. Methods: We retrospectively reviewed our database to identify patients who underwent focal resection after confirmation of multiple seizure foci on intracranial EEG. Results of presurgical investigations, intracranial EEG, procedures, complications and outcome were collected. Results: A total of 17 patients underwent palliative resection (8 left, 9 right). Preoperative MRI revealed malformations of cortical development in 6 patients, and MTS in 6 patients. Intracranial stereo EEG revealed 8 bilateral and 9 unilateral multifocal epileptogenic foci. Surgical procedures included anterior temporal lobectomy (ATL) or selective amygdalohippocampectomy in 4 patients, ATL plus additional cortical resection in 7 patients, and extratemporal resection in 6 patients. One patient had dysphasia post ATL and a second patient had worsened cognitive dysfunction post extended frontal lobectomy. Favorable seizure outcome (Engel class I and II) was achieved in 10 patients (58.8%). Pathology revealed focal cortical dysplasia in 6 patients and hippocampal sclerosis in 5 patients. Conclusions: Palliative resection of a dominant epileptogenic focus confirmed by intracranial EEG is effective in carefully selected adult cases of intractable epilepsy, particularly in patients with lesional epilepsy.
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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 | 0.002 |
| 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".