Radiofrequency Thermocoagulation in Refractory Focal Epilepsy: The Montreal Neurological Institute Experience
Bibliographic record
Abstract
BACKGROUND: Radiofrequency thermocoagulation (RF-TC) is a minimally invasive ablative option for refractory focal epilepsy. METHODS: A retrospective chart review was conducted of all patients who underwent stereoelectroencephalography (SEEG)-guided RF-TC at our institution. RESULTS: Fourteen patients underwent robot-guided electrode implantation and subsequent RF-TC. After RF-TC, one of the three patients with PVNH was seizure free, one had 18 months of seizure freedom (Engel 2b), and one required temporal neocortical/PVNH resection (Engel 1a). One of the four patients with focal cortical dysplasia (FCD) was seizure free (Engel 1a), two attained seizure freedom after resection (Engel 1a and 1b), while one continues to have significant seizures (Engel 4b). One patient with cavernoma and low central area epileptogenic zone (EZ) did not benefit from RF-TC and is planned for resection. Two of the MRI-negative patients achieved seizure freedom for 3 months and 1 year, respectively, subsequently requiring resection (Engel 1a). One remains seizure free at 4 weeks. Three had seizure recurrence immediately (Engel 4b). With RF-TC alone, two patients (14%) achieved Engel 1a, two were seizure free at 1 year, one had 3 months of seizure freedom, while the rest had recurrence immediately or within a few weeks. 7/14 patients underwent secondary interventions after RF-TC. Overall, seven patients achieved Engel 1a or 1b, one each 2b and 3a, and five Engel 4b. CONCLUSION: At our institution, RF-TC is a safe ablative procedure for refractory focal epilepsy. It can serve as a segue to secondary interventions and appears promising in PVNH cases. Its role in MRI-negative cases is less clear.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".