Dorsal Root Ganglion Stimulation for Postherpetic Neuralgia: A Pilot Scoping Review of the Current Evidence
Bibliographic record
Abstract
This review synthesizes current evidence regarding the use of dorsal root ganglion (DRG) stimulation, a form of pain neuromodulation, for managing postherpetic neuralgia (PHN), a debilitating chronic neuropathic pain syndrome arising from herpes zoster virus infection. Comprehensive searches across multiple databases (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library) for studies published from 2010 onward were supplemented by manual reference checks and gray literature review. Inclusion criteria encompassed adults with PHN (pain persisting ≥ three months post-rash onset) treated with DRG stimulation involving varying lead placements and stimulation techniques. Two independent reviewers screened articles and extracted data, resolving disagreements through consensus or a third reviewer, with a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram documenting study selection, resulting in 23 included studies after screening 117 initial references. Extracted data revealed variability in DRG stimulation techniques (percutaneous vs. surgical lead placement) and parameters (tonic, burst, high-frequency), with reported outcomes including pain scores, quality of life, opioid use, and complications. While case series support DRG efficacy for PHN, studies are limited by small sample sizes and a lack of prospective or randomized trials focused exclusively on PHN. Many studies included mixed-pain populations, complicating direct comparisons. Broader neuromodulation evidence suggests DRG may cause fewer paresthesias than spinal cord stimulation (SCS). Some studies indicate DRG may be more effective for other neuropathic pain types than for PHN. This review identifies significant variability and substantial gaps in the literature, notably the absence of rigorous randomized controlled trials and studies focusing solely on PHN, underscoring the need for standardized protocols and targeted clinical trials. Although DRG stimulation shows potential for managing PHN, its comparative effectiveness relative to other pain etiologies warrants further investigation to refine treatment approaches and optimize patient outcomes.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".