Stereotactic Laser Ablation vs Open Corpus Callosotomy: A Systematic Review and Meta-Analysis of Individual Patient Data
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study compares seizure outcomes and complication rates between open corpus callosotomy (CC) and laser interstitial thermal therapy (LITT) CC in adult and pediatric patients with medically refractory generalized epilepsy. Although CC is a palliative option for nonresectable foci, the efficacy and safety of LITT CC compared with open CC remain unclear. METHODS: We conducted a systematic review and individual patient data meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies reported individual seizure outcomes for open or LITT CC patients. Studies involving concurrent resection or neuromodulation were excluded. RESULTS: We analyzed 72 studies (948 patients: 858 open CC, 90 LITT CC) published between January 1980 and February 2024. Overall seizure freedom rates were similar (LITT CC: 14.5% vs open CC: 19.95%; P = .25), as were drop attack freedom (51.3% vs 45.1%; P = .34) and >50% seizure reduction rates (52.6% vs 55.2%; P = .78). LITT CC had shorter hospital stays (median: 2 vs 6 days; P < .0001) but with more subsequent epilepsy surgeries (24.4% vs 10.1%; P = .0003). Complication rates were comparable (LITT CC: 31.3% vs open CC: 22.9%; P = .1), but LITT CC with 1-2 trajectories had significantly lower complications than those with 3 to 5 trajectories (13.2% vs 54.8%, P = .0003). Neither intervention type nor callosotomy extent influenced seizure outcomes in multivariable logistic regression analyses, stratified by age and follow-up duration. In adults, vagal nerve stimulation before CC was an independent predictor of drop attack freedom (odds ratio = 3.51 [1.16-11.58], P = .03) and Lennox-Gastaut syndrome was an independent predictor of seizure freedom (odds ratio = 3.65 [1.35-10.33], P = .01). CONCLUSION: Both procedures offer comparable effectiveness, suggesting LITT CC is a viable minimally invasive alternative; further experience is needed.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".