Evidence-Based Guideline Update: Vagus Nerve Stimulation for the Treatment of Epilepsy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the evidence since the 1999 assessment regarding efficacy and safety of vagus nerve stimulation (VNS) for epilepsy, currently approved as adjunctive therapy for partial-onset seizures in patients >12 years. METHODS: We reviewed the literature and identified relevant published studies. We classified these studies according to the American Academy of Neurology evidence-based methodology. RESULTS: VNS is associated with a >50% seizure reduction in 55% (95% confidence interval [CI] 50%-59%) of 470 children with partial or generalized epilepsy (13 Class III studies). VNS is associated with a >50% seizure reduction in 55% (95% CI 46%-64%) of 113 patients with Lennox-Gastaut syndrome (LGS) (4 Class III studies). VNS is associated with an increase in ≥50% seizure frequency reduction rates of ~7% from 1 to 5 years postim-plantation (2 Class III studies). VNS is associated with a significant improvement in standard mood scales in 31 adults with epilepsy (2 Class III studies). Infection risk at the VNS implantation site in children is increased relative to that in adults (odds ratio 3.4, 95% CI 1.0-11.2). VNS is possibly effective for seizures (both partial and generalized) in children, for LGS-associated seizures, and for mood problems in adults with epilepsy. VNS may have improved efficacy over time. RECOMMENDATIONS: VNS may be considered for seizures in children, for LGS-associated seizures, and for improving mood in adults with epilepsy (Level C). VNS may be considered to have improved efficacy over time (Level C). Children should be carefully monitored for site infection after VNS implantation. Neurology® 2013;81:1-7.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".