Corpus callosotomy in children with intractable epilepsy using frameless stereotactic neuronavigation: 12-year experience at The Hospital for Sick Children in Toronto
Bibliographic record
Abstract
OBJECT: Although corpus callosotomy has been used effectively since the late 1930s to treat severe, medically intractable seizure disorders, particularly atonic or drop-attack seizures, controversy remains as to when, how, and how much surgery should be performed. Intraoperative determination of the extent of callosotomy, the need to stage the procedure, and the side of the interhemispheric approach represent technical issues that remain debatable. The authors report the 12-year experience of the senior author as well as surgical outcomes with corpus callosotomy using a frameless stereotactic neuronavigation system (ISG View Wand and BrainLab). METHODS: Thirteen consecutive children at The Hospital for Sick Children underwent single-stage corpus callosotomy for medically intractable seizures. The mean age was 10.3 years. Five children underwent partial callosotomy, and 8 underwent complete callosotomy. The side of operative approach to avoid large parasagittal bridging veins was determined by preoperative study of 3D MR imaging/MR venography reconstructed by the neuronavigation system. The extent of callosotomy was determined using intraoperative feedback from the neuronavigation system and postoperative MR imaging. RESULTS: The extent of callosotomy determined by intraoperative neuronavigation and postoperative MR imaging correlated closely in all cases. There were no operative deaths. There was no significant postoperative morbidity related to venous infarction. Four of 5 patients in the partial callosotomy cohort and 7 of 8 patients in the complete callosotomy cohort showed significant improvement in seizure control. CONCLUSIONS: The use of frameless stereotactic neuronavigation is a safe, effective, and important surgical adjunct in the planning and execution of successful corpus callosotomy in children with intractable 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".