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 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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".