B.03 Safety and effectiveness of insular resections for drug-resistant epilepsy
Bibliographic record
Abstract
Background: Insular cortex involvement as a part of epileptogenic zone is often suspected in the context of operculo-insular semiology and can be confirmed by routine interrogation of the insula with stereo-electroencephalography (SEEG). However the safety and efficacy of insular resections remains unclear. Methods: We reviewed all the patients who underwent insular resection for drug-resistant epilepsy, from 2002 – 2016, in the Calgary Epilepsy Program. Details of the comprehensive pre-surgical evaluation, surgery performed, complications and seizure outcome at the latest follow-up were collected. Results: Fifteen patients (8 males, 7 females) with age range 3 – 41 years were identified. MRI was normal in 9 patients. The decision to resect the Insula was made based on clinical semiology and structural and functional imaging in 6 patients and on SEEG findings in 9 patients. Insular resection was total in 11 and partial in 4 patients. Four (26%) patients had transient hemiparesis and 1 patient had permanent mild upper extremity weakness following total resection. After a mean follow-up period of 45.6 months (range 2 – 150 months), 40% of the patients are seizure free. Conclusions: Insular cortex resections for drug resistant epilepsy can be performed safely and may contribute to additional effectiveness in seizure outcomes in patients with challenging extra-temporal 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 | 0.001 |
| 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".