Efficacy of Vagal Nerve Stimulation for Drug-Resistant Epilepsy: Is it the Stimulation or Medication?
Bibliographic record
Abstract
BACKGROUND: Vagus nerve stimulation (VNS) therapy has been widely recognized as an alternative for the treatment of drug-resistant epilepsy, although modification of antiepileptic drugs (AEDs) during VNS treatment could explain the improvement in patients. METHODS: We retrospectively assessed the efficacy of VNS in 30 adult patients with epilepsy treated with >6 months of follow-up. The criteria for implantation were the following: (1) not a candidate for resective epilepsy surgery, (2) drug-resistant epilepsy, (3) impairment of quality of life, (4) no other option of treatment, and (5) patients with idiopathic generalized epilepsy who fail to be controlled with appropriate AEDs. We assessed sociodemographics, seizure etiology, seizure classification, and AEDs used during treatment with VNS. We assessed adverse effects and efficacy. Responder rate was defined as >50% seizure improvement from baseline. RESULTS: Thirty patients (females, 18; males, 12; age, 35.1±13.3 years) were included. After 6, 12, 24, and 36 months of follow-up, the response rates were: 13/30 (43%), 13/27 (48%), 9/22 (41%), and 8/16 (50%), respectively; none was seizure free. Fifty-seven percent, 33%, 59%, and 81% of patients had changes of medication type or dose at 6, 12, 24, and 36 months respectively. In the majority of patients, the change of medication consisted of an increase in the dose of AEDs. CONCLUSIONS: Our study shows that VNS is an effective therapy, although significant changes in medications were done along with the therapy; therefore, the real effect of VNS could be controversial.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".