Stellate ganglion block beyond chronic pain: A literature review on its application in painful and non-painful conditions
Bibliographic record
Abstract
Cervical sympathetic or stellate ganglion blocks (SGBs) have been commonly used in the treatment of painful conditions like complex regional pain syndrome (CRPS). However, there is literature to suggest its utility in managing non-painful conditions as well. The focus of this literature review is to provide an overview of indications for SGB for painful and non-painful conditions. We identified published journal articles in the past 25 years from Embase and PubMed databases with the keywords "cervical sympathetic block, stellate ganglion blocks, cervical sympathetic chain, and cervical sympathetic trunk". A total of 1556 articles were obtained from a literature search among which 311 articles were reviewed. Among painful conditions, there is a lack of evidence in favor of or against the use of SGB for CRPS despite its common use. SGB can provide postoperative analgesia in selective surgeries and can be effective in temporary pain control of refractory angina and the acute phase of herpes zoster infection. Among non-painful conditions, SGB may have beneficial effects on the management of post-traumatic stress disorder (PTSD), refractory ventricular arrhythmias, hot flashes in postmenopausal women, and breast cancer-related lymphedema. Additionally, there have been various case reports illustrating the benefits of SGB in the management of cerebral vasospasm, upper limb erythromelalgia, thalamic and central post-stroke pain, palmar hyperhidrosis, orofacial pain, etc. In our review of literature, we found that SGB can be useful in the management of various non-painful conditions beyond the well-known treatment for CRPS, although further studies are required to prove its efficacy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".