Epilepsy Surgeries Requiring an Operculoinsular Cortectomy: Operative Technique and Results
Bibliographic record
Abstract
BACKGROUND: Epilepsy surgeries requiring an operculoinsulectomy pose significant difficulties because the perisylvian area is highly vascular, deep, and functional. OBJECTIVE: To report the operative technique and results of epilepsy surgeries requiring an operculoinsular cortectomy at our institution. METHODS: The data of all consecutive patients who had undergone an epilepsy surgery requiring an operculoinsular cortectomy with a minimum follow-up of 1 yr were reviewed. Tumor and vascular malformation cases were excluded. Surgical techniques are described based on findings during surgery. RESULTS: Twenty-five patients underwent an epilepsy surgery requiring an operculoinsular cortectomy: mean age at surgery was 35 y (9-51), mean duration of epilepsy was 19 y (5-36), 14 were female, and mean duration of follow-up was 4.7 y (1-16). Magnetic resonance imaging of the operculoinsular area was normal or revealed questionable nonspecific findings in 72% of cases. Investigation with intracranial EEG electrodes was done in 17 patients. Surgery was performed on the dominant side for language in 7 patients. An opercular resection was performed in all but 2 patients who only had an insulectomy. Engel class I seizure control was achieved in 80% of patients. Postoperative neurological deficits (paresis, dysphasia, alteration of taste, smell, hearing, pain, and thermal perceptions) were frequent (75%) but always transient except for 1 patient with persistent mild alteration of thermal and pain perception. CONCLUSION: Surgical treatment of operculoinsular epilepsy is effective in achieving seizure control and is associated with an acceptable long-term complication rate.
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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 | 0.001 | 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".