Seizure outcome of pediatric epilepsy surgery: systematic review and meta-analyses
Bibliographic record
Abstract
Objective: This systematic review and meta-analyses assessed seizure outcome following pediatric epilepsy surgery. Methods: MEDLINE, Embase and Cochrane were searched for pediatric epilepsy surgery original research from 1990 to 2017. The outcome was seizure freedom at 12 months or longer follow-up. Using random effects models, the effect sizes for controlled studies, uncontrolled studies on surgery locations (temporal lobe [TL], extra-temporal lobe [ETL] or hemispheric surgery), pathologies, non-lesional epilepsy and incomplete resection were estimated. Meta-regression assessed the relationship between age at surgery, age at seizure onset and seizure outcome. Random-effects network meta-analysis was conducted for surgery locations. Results: 258 studies were included. Surgery achieved higher seizure freedom than medical therapy (OR=6.49 [95%CI: 2.87, 14.70], p<0.001). Seizure freedom declined over time after surgery, from 64.8% (95%CI: 51.2%, 76.4%; p=0.034) at 1 year, to 60.3% (95%CI: 52.9%, 67.4%; p=0.007) at 5 years, and 39.7% (95%CI: 28.4%, 52.2%, p=0.106) at 10 years. Seizure freedom was (i) highest for hemispheric surgery, followed by TL, and ETL surgery; and (ii) highest for tumor, and lower for malformations of cortical development. Seizure freedom was lower for non-lesional than lesional epilepsy (OR=0.54 [95%CI: 0.34, 0.88], p=0.013), and incomplete than complete resection (OR=0.13 [95%CI: 0.08, 0.21], p<0.001). Age at surgery and age at seizure onset were associated with seizure freedom for mixed pathologies and surgery locations, and TL surgery. Conclusion: Epilepsy surgery was more effective than medical therapy to control seizures. Understanding seizure outcomes of different surgery locations, pathologies, non-lesional epilepsy and incomplete resection will assist with pre-surgical counselling.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.130 |
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; both teacher heads agree on what is shown here.
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".