Electrocorticography-Guided Resection Enhances Postoperative Seizure Freedom in Low-Grade Tumor-Associated Epilepsy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Low-grade cerebral neoplasms are commonly associated with medically intractable epilepsy. Despite increasing evidence that epileptogenic brain regions commonly extend beyond visible tumor margins, the utility of extended surgical resections leveraging intraoperative electrocorticography (ECoG) remains unclear. OBJECTIVE: To determine whether ECoG-guided surgery is associated with improved postoperative seizure control. METHODS: We performed a systematic review and meta-analysis encompassing both adult and pediatric populations. The primary outcome measure was postoperative seizure freedom as defined by Engel class I outcome. Class I/II outcome served as a secondary measure. Relevant clinical and operative data were recorded. A random-effects meta-analysis based on the pooled odds ratio (OR) of seizure freedom was performed on studies that reported comparative data between ECoG-guided surgery and lesionectomy. RESULTS: A total of 31 studies encompassing 1115 patients with medically refractory epilepsy met inclusion criteria. Seven studies reported comparative data between ECoG-guided surgery and lesionectomy for meta-analysis. Tumor resection guided by ECoG was associated with significantly greater postoperative seizure freedom (OR 3.95, 95% CI 2.32-6.72, P < .0001) and class I/II outcome (OR 5.10, 95% CI 1.97-13.18, P = .0008) compared with lesionectomy. Postoperative adverse events were rare in both groups. CONCLUSION: These findings provide support for the utilization of ECoG-guided surgery to improve postoperative seizure freedom in cases of refractory epilepsy associated with low-grade neoplasms. However, this effect may be attenuated in the presence of concomitant cortical dysplasia, highlighting a need for improved presurgical and intraoperative monitoring for these most challenging cases of localization-related epilepsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".