P.023 Neurostimulation in Drug-Resistant Epilepsy: Systematic Review and Meta-Analysis from the ILAE Evidence-Based Epilepsy Surgery Task Force
Bibliographic record
Abstract
Background: Drug-resistant epilepsy (DRE) can affect up to one third of individuals with epilepsy. We conducted a systematic review and meta-analysis of vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS) in patients with DRE to summarize the current evidence on efficacy and tolerability for these neuromodulation modalities. Methods: We searched three online databases with a pre-specified search strategy. We included published randomized controlled trials (RCT) and their open-label extension studies, as well as prospective case series, with samples greater than 20 participants, reporting efficacy and tolerability. Results: We identified 31 studies, six of which are RCTs and 25 prospective observational studies. At long term follow-up, five observational studies for VNS reported a pooled mean decrease in seizure frequency at last follow-up of 35%. In the extension studies for RNS, the median seizure reduction was 53%, 66% and 75.0% at two, five and nine years respectively. For DBS, the median reduction was then 56%, 69% and 75% at two, five and seven years respectively. Conclusions: Neurostimulation modalities are effective for the treatment of DRE, with improving outcomes over time and few major complications. Higher quality long-term data on DBS and RNS suggest larger seizure reduction rates than VNS.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".