A.6 Vagus Nerve Stimulation in patients with therapy resistant generalized epilepsy
Bibliographic record
Abstract
Background: For patients with generalized epilepsy who do not respond to anti-seizure medications, the therapeutic options are limited. Vagus nerve stimulation (VNS) is a treatment mainly approved for therapy resistant focal epilepsy. There is limited information on the use of VNS on generalized epilepsies, including Lennox Gastaut Syndrome(LGS) and genetic generalized epilepsy(GGE). Methods: We identified patients with a diagnosis of Lennox-Gastaut Syndrome or Genetic Generalized Epilepsy, who underwent VNS implantation, between1997 and July 2018. Results: A total of 46 patients were included in this study with a history of therapy resistant generalized epilepsy. The mean age at implantation was 24 years(IQR= 17.8-31 years) and 50%(n=23) were female. The most common etiologies were GGE in 37%(n=17) and LGS in 63%(n=29). Median follow-up since VNS implantation was 63 months(IQR:31-112.8months). 41.7%(n=12) of the LGS group became responders, and 64.7%(n=11) in the GGE group. The best response in seizure reduction was seen in generalized tonic-clonic seizures. There was a reduction of seizure-related hospital admissions from 89.7%(N=26) pre-implantation, to 41.4%(N=12) post-implantation (p<0.0001). The frequency of side effects due to the stimulation was similar in both groups(62.1% in LGS and 61.1% in GGE). Conclusions: VNS is an effective treatment in patients with therapy resistant generalized epilepsy, especially GGE.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".