Gamma Knife Surgery for Refractory Insular Cortex Epilepsy
Bibliographic record
Abstract
BACKGROUND: Resection of the epileptogenic insular cortex is surgically challenging. We sought to evaluate the potential of Gamma Knife surgery (GKS) for the treatment of pharmacoresistant insular cortex epilepsy (ICE) in patients who underwent GKS between 2005 and 2010. Adverse events and effects on seizure frequency were assessed. METHODS: Three cases of refractory ICE were treated by GKS between 2005 and 2010. RESULTS: Case 1 had refractory nonlesional ICE, proven by depth electrodes only partially helped by a left posterior insulectomy. Case 2 had refractory ICE due to a left insular cavernoma. Case 3 had refractory ICE, confirmed by an invasive study but complicated by transient dysphasia from contusion of Wernicke's area. The marginal and maximum radiation doses delivered were 20 and 40 Gy, respectively. Treatment volume ranged from 1.2 to 3.2 cm3. Two out of 3 patients experienced significant seizure reduction and the third had a worthwhile improvement (follow-up 30-76 months). Complete antiepileptic drug withdrawal was un-fortunately not possible. Complications included transient lightheadedness and new-onset seizures responsive to medical treatment, both in the same patient. CONCLUSION: GKS is a promising technique for selected drug-resistant ICE patients. Additional observations are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".