Surgical outcomes for medically intractable epilepsy in low- and middle-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe the current state of epilepsy surgery and establish estimates of seizure outcomes following surgery for medically intractable epilepsy (MIE) in low- and middle-income countries (LMICs). METHODS: The MEDLINE and Embase databases were searched without publication date restriction. This search was supplemented by a manual screen of key epilepsy and neurosurgical journals (January 2005 to December 2016). Studies that reported outcomes for at least 10 patients of any age undergoing surgery for MIE in LMICs over a defined follow-up period were included. A meta-analysis with a random-effects model was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement and MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines. Pooled estimates of seizure freedom and favorable seizure outcomes following anterior temporal lobectomy with or without amygdalohippocampectomy (ATL ± AH) were reported. RESULTS: Twenty studies were selected, of which 16 were from Asian centers. The average age at surgery in all studies was less than 30 years, and the average preoperative duration of epilepsy ranged from 3 to 16.1 years. Mesial temporal sclerosis accounted for 437 of 951 described pathologies, and 1294 of the 1773 procedures were ATL ± AH. Based on 7 studies (646 patients) the pooled seizure freedom estimate following ATL ± AH was 68% (95% CI 55%-82%). Based on 8 studies (1096 patients), the pooled estimate for favorable seizure outcomes was 79% (95% CI 74%-85%). CONCLUSIONS: Surgery for MIE in LMICs shows a high percentage of seizure freedom and favorable outcomes. These findings call for a concerted global effort to improve timely access to surgery for MIE patients in these regions, including investments aimed at refining existing and establishing additional centers.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.003 | 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".