Stellate Ganglion Block for Psychiatric Disorders: A Systematic Review of the Clinical Research Landscape
Bibliographic record
Abstract
Stellate ganglion block (SGB) is a procedure involving the injection of a local anesthetic surrounding the stellate ganglion to inhibit sympathetic outflow. The objective of this review was to summarize existing evidence on the use of SGB in adults with psychiatric disorders. A systematic search identified 17 published studies and 4 registered clinical trials. Eighty-eight percent of published studies, including 2 randomized controlled trials (RCTs), used SGB for posttraumatic stress disorder (PTSD), although its use for schizophrenia spectrum disorders was also explored. Administration of 1 to 2 SGBs using right-sided laterality with 0.5% ropivacaine was most common. Preliminary evidence from clinical trials and case studies supports the feasibility of SGB for treating psychiatric disorders involving dysregulation of the sympathetic nervous system, although effectiveness evidence from RCTs is mixed. One RCT concluded that improvement in PTSD symptoms was significant, while the other concluded that it was nonsignificant. Improvements were noted within 5 minutes of SGB and lasted 1 month or longer. Registered clinical trials are exploring the use of SGB in new psychiatric disorders, including major depressive disorder and borderline personality disorder. More studies with larger sample sizes and alternate protocols are needed to further explore therapeutic potential of SGB for psychiatric disorders.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".