Efficacy of Stellate Ganglion Blockade Applied with Light Irradiation
Bibliographic record
Abstract
OBJECTIVE: Stellate ganglion block has mostly been used to relieve symptoms of neuropathic pain; several potential complications have been reported. Noninvasive stellate ganglion block application using light irradiation (SG-LI) can be used as an alternative to conventional injection blockades. Based on the variety of application protocols among previous studies, it was needed to further identify the clinical efficacy of SG-LI in managing neuropathic pain or other disorders associated with sympathetic hyperactivity. DESIGN: A comprehensive search of online databases was performed to identify experimental or observational studies reporting the efficacy of SG-LI in treating patients with disorders requiring sympatholytic management. The included studies were subjected to a meta-analysis and risk-of-bias assessment. RESULTS: Twenty-one experimental studies with a Physiotherapy Evidence Database score of 6/10 and 5 observational studies with a Newcastle-Ottawa scale score of 7/9 were included in the analysis. A significant effect on pain relief favoring SG-LI was identified at a standard mean difference (SMD) of -2.05 [95% confidence interval (CI), -2.49 to -1.61; P < 0.00001]. Similar effects favoring SG-LI were found in peripheral blood flow (SMD, 1.26; 95% CI, 0.26-2.25; P = 0.01) and skin temperature (SMD, 1.31; 95% CI, 0.55, 2.08; P = 0.0007). CONCLUSIONS: Stellate ganglion block application using light irradiation effectively relieves pain of various etiologies and successfully induces a sympatholytic response. Stellate ganglion block application using light irradiation may be a valuable addition to the contemporary pain management armamentarium.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".