Transcutaneous vagal nerve stimulation for the treatment of trauma- and stressor-related disorders: systematic review of randomised controlled studies
Bibliographic record
Abstract
BACKGROUND: Vagal nerve stimulation (VNS) has recently emerged as a prospective therapeutic approach for addressing trauma- and stressor-related disorders (TSRDs). AIMS: We assessed findings from randomised controlled trials for the safety and efficacy of VNS as a viable treatment for TSRDs. METHOD: We systematically searched Medline, Embase, PsycINFO, CINAHL, Web of Science, Cochrane Central databases, trial registries, preprint servers and Google Scholar from inception to December 2023. Rayyan software was used for screening procedures. Two reviewers independently completed data extraction based on the inclusion criteria. RESULTS: We synthesised data by using a narrative approach. A total of 322 abstracts were identified and assessed, and seven studies were included in the review. Based on evidence synthesis, the present state of VNS as a treatment intervention for TSRDs, namely post-traumatic stress disorder (PTSD), is limited and does not meet clinical expectations. The overall certainty of evidence was very low. However, evidence shows that VNS may alter and reduce specific aspects associated with PTSD phenomenology, including the reduction of anger responses and the attenuation of hyperarousal during psychological interventions. CONCLUSIONS: Although preliminary analyses provide evidence that transcutaneous VNS temporarily increases parasympathetic activity under specific conditions, these effects appear to be short-lasting, and the impact of repeated administration on long-term autonomic function remains unknown. Future randomised control trials should evaluate the therapeutic efficacy of VNS for treating TSRDs.
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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 | 0.025 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".