Vagus nerve stimulation in depression
Bibliographic record
Abstract
Ever since the introduction of chemical and electrical convulsive treatment for psychiatric disorders in the 1930s and 1940s, biological techniques have been used extensively in the amelioration of a variety of psychiatric disorders. Techniques of recent vintage have included transcranial magnetic stimulation, deep brain stimulation and vagus nerve stimulation (VNS). Since VNS attenuates seizures in animal models, the treatment was initially developed and approved by the FDA for treatment of drug-resistant partial-onset epilepsy. Additional data, including the known neuroanatomy of the vagus nerve, effects of VNS on monoamines and mood improvement in patients with epilepsy who were treated with VNS, provided a rationale for further investigation in patients with primary mood disorders. VNS has been administered acutely for 10 weeks to 60 patients with treatment-resistant depression. Longer-term follow-up data has been analysed for the first 30 patients. Response rates have been at least 30% in the acute study. Similar to findings in epilepsy and in contrast to the usual results of long-term medication trials, longer term data regarding symptomatic and functional outcomes of depressed patients receiving VNS continue to look promising. As opposed to electroconvulsive therapy, VNS is not associated with cognitive impairment. These results have led to approval of VNS for the treatment of resistant depression (unipolar or bipolar) in both Europe and Canada. Currently, a pivotal double-blind acute study is underway in the US.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".