Vagus Nerve Stimulation in Autoimmune Conditions: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Systemic autoimmune conditions are characterized by increased inflammation and high disease burden. Vagus nerve stimulation (VNS) is known to activate the cholinergic anti-inflammatory pathway and can serve as a potential therapeutic modality for autoimmune conditions. This study aimed to conduct a preregistered systematic review to determine the effect of VNS on inflammation in autoimmune conditions, according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. METHODS: The databases of Medline/PubMed, Web of Science, Scopus, and CENTRAL were searched until September 2, 2024. Data pertaining to changes in inflammatory blood biomarkers and clinical outcomes in patients with autoimmune conditions receiving VNS were extracted. Studies were included if they provided measurements of peripheral blood biomarkers and clinical outcomes. Study screenings and full-text article reviews were conducted in duplicate. The design of the included studies was assessed using the National Health Lung and Blood Institute guidelines. RESULTS: Twelve clinical trials studying the effect of VNS on rheumatoid arthritis, Crohn disease, polymyalgia rheumatica, psoriatic arthritis, ankylosing spondylitis, systemic lupus erythematous, and systemic sclerosis were reviewed. We found that >50% of studies found a reduction in the mean difference of pro-inflammatory cytokine levels before and after VNS (C-reactive protein decreased in 6 of 9 studies; tumor necrosis factor α decreased in 4 of 8 studies) with the most consistent reduction in IL-6 levels (6 of 7 studies). CONCLUSION: Although it can be suggested that VNS can decrease markers of pro-inflammation in autoimmune diseases, more clinical studies with robust design and quality are needed to more confidently support VNS as a therapeutic option for autoimmune conditions.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".