Added value of corpus callosotomy following vagus nerve stimulation in children with <scp>Lennox–Gastaut</scp> syndrome: A multicenter, multinational study
Bibliographic record
Abstract
OBJECTIVE: Lennox-Gastaut syndrome (LGS) is a severe form of epileptic encephalopathy, presenting during the first years of life, and is very resistant to treatment. Once medical therapy has failed, palliative surgeries such as vagus nerve stimulation (VNS) or corpus callosotomy (CC) are considered. Although CC is more effective than VNS as the primary neurosurgical treatment for LGS-associated drop attacks, there are limited data regarding the added value of CC following VNS. This study aimed to assess the effectiveness of CC preceded by VNS. METHODS: This multinational, multicenter retrospective study focuses on LGS children who underwent CC before the age of 18 years, following prior VNS, which failed to achieve satisfactory seizure control. Collected data included epilepsy characteristics, surgical details, epilepsy outcomes, and complications. The primary outcome of this study was a 50% reduction in drop attacks. RESULTS: A total of 127 cases were reviewed (80 males). The median age at epilepsy onset was 6 months (interquartile range [IQR] = 3.12-22.75). The median age at VNS surgery was 7 years (IQR = 4-10), and CC was performed at a median age of 11 years (IQR = 8.76-15). The dominant seizure type was drop attacks (tonic or atonic) in 102 patients. Eighty-six patients underwent a single-stage complete CC, and 41 an anterior callosotomy. Ten patients who did not initially have a complete CC underwent a second surgery for completion of CC due to seizure persistence. Overall, there was at least a 50% reduction in drop attacks and other seizures in 83% and 60%, respectively. Permanent morbidity occurred in 1.5%, with no mortality. SIGNIFICANCE: CC is vital in seizure control in children with LGS in whom VNS has failed. Surgical risks are low. A complete CC has a tendency toward better effectiveness than anterior CC for some seizure types.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".