Technical Neurosurgical Aspects of Pediatric Epilepsy Surgery Including Resections and Disconnections
Bibliographic record
Abstract
Background: Epilepsy remains one of the most common neurological disorders in children, with approximately one-third of patients developing drug-resistant epilepsy (DRE) that may require surgical intervention. This review outlines the technical neurosurgical aspects of pediatric epilepsy surgery, focusing on resective and disconnection procedures. Summary: Successful epilepsy surgery relies on accurate identification of the hypothesized epileptogenic zone through comprehensive presurgical evaluation including clinical assessment, seizure semiology analysis, magnetic resonance imaging, electroencephalography (EEG), and neuropsychological testing. In complex cases, additional imaging modalities such as FDG-PET, SPECT, and MEG help establish the anatomo-electric-clinical network. Invasive monitoring using subdural grids or stereotactic EEG provides critical data for surgical planning in cases with discordant findings, potential involvement of eloquent tissue, or non-lesional epilepsy. Resective surgery is the primary intervention for focal, lesional epilepsy, with seizure freedom rates varying by location and etiology (70-80% for temporal lobe and tumor-associated epilepsy; 60% for extra-temporal lobe; 51% for non-lesional cases). Anterior temporal lobectomy, a cornerstone procedure, involves careful consideration of hemisphere dominance and selective approaches to mesial structures. Extent of resection is a critical determinant of outcome, with incomplete removal of epileptogenic tissue consistently identified as the leading cause of surgical failure. Disconnection procedures include anterior and posterior quadrant disconnections, hemispherectomy, and corpus callosotomy. Anterior quadrant disconnection isolates seizure foci within the frontal lobe, whereas posterior quadrant disconnection targets the temporal, parietal, and occipital lobes. Functional hemispherotomy has largely replaced anatomic hemispherectomy because it has a lower complication profile but maintains equivalent seizure freedom rates. Corpus callosotomy primarily targets generalized seizures, especially atonic seizures leading to drop attacks. Key Messages: Recent advances include minimally invasive techniques (such as laser ablation) and neuromodulation approaches (such as responsive neurostimulation and deep brain stimulation). Future directions will likely incorporate higher resolution imaging technologies and artificial intelligence driven signal processing to optimize outcomes and offer personalized treatment strategies that improve seizure control and quality of life for children with DRE. .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".