Electrostimulation for Refractory Epilepsy: A Review
Bibliographic record
Abstract
Epilepsy is a neurological disorder that has been diagnosed in approximately 1% of the world's population. In North America alone, more than 3 million individuals suffer from epilepsy. Antiepileptic drugs are not fully effective in some patients, and most drugs have adverse side effects. Recently, several stimulation techniques (responsive neural, vagal nerve, transcranial magnetic, and deep brain) have been used as adjunct therapies to treat medically refractive seizures. Since its Food and Drug Administration approval in 2013, responsive neural stimulation (RNS), a closed-loop electrical stimulation system, has emerged as a potential therapeutic alternative to treat patients with epilepsy (PWE). RNS consists of a cranially implantable neurostimulator that sends electrical pulses using depth electrodes to epileptic foci/focus after the device senses irregular electrical activity, thus avoiding the onset of a seizure. In a long-term study that lasted 7 yr and involved more than 245 patients using RNS, results showed that16% of patients were seizure free, 60% had 50% or greater seizure reduction, and 84% had some improvement. Quality of life improved in 44% of the patients by the end of the second year. There is a need for more, larger, well-designed, randomized, controlled trials to validate and optimize efficacy and safety of invasive intracranial neurostimulation treatments in PWE. This article highlights the effects of treating patients with medically refractive seizures using RNS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".