Vagus nerve stimulation in patients with therapy-resistant generalized epilepsy.
Bibliographic record
Abstract
BACKGROUND: For patients with generalized epilepsy who do not respond to antiseizure 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).\nMETHODS: We identified patients with a diagnosis of generalized epilepsy (including LGS and GGE), who underwent VNS implantation at the London Health Sciences Centre and Western University, London, Ontario, since this treatment became available in Canada in 1997 until July 2018. We assessed response to the treatment, including admissions to hospital and complications.\nRESULTS: 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 (interquartile range [IQR] = 17.8-31 years), significantly younger in the LGS group (p = 0.02) 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: 31-112.8 months). Of the LGS group 41.7% (n = 12) of patients had an overall seizure reduction of 50% or more, and 64.7% (n = 11) in the GGE group without statistical significance between the groups. The best response in seizure reduction was seen in generalized tonic-clonic seizures, with a significant reduction in the GGE group (p = 0.043). There was a reduction of seizure-related hospital admissions from 91.3% (N = 42) preimplantation, to 43.5% (N = 20) postimplantation (p < 0.05). The frequency of side effects due to the stimulation was almost equal in both groups (62.1% in LGS and 64.7% in GGE).\nCONCLUSIONS: Vagus nerve stimulation should be considered as a treatment in patients with therapy-resistant generalized epilepsy, especially in cases with 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".