Post-herpetic neuralgia: A systematic review of current interventional pain management strategies
Bibliographic record
Abstract
BACKGROUND: Post-herpetic neuralgia (PHN) is usually a constant or intermittent burning, stabbing, or sharp shooting pain with hyperalgesia or allodynia, persisting beyond the healing of herpetic skin lesions. This review was carried out in concordance to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We used PICOS (Population, Intervention, Control, and Outcome Study) design for inclusion of potential studies into this review. Online literature available in PubMed, Cochrane, and Embase was searched for studies from January 1995 till March 2020, which evaluated interventional treatments in PHN by an independent reviewer, using the relevant medical subject heading (MeSH) terms. We analyzed the following outcome parameters with regard to each intervention-pain status at predefined fixed intervals after the intervention, quality of sleep using any of the reported questionnaires, analgesic consumption, functional evaluation, and quality of life assessment after the intervention. CONCLUSION: Interventional pain management options provide effective and long-lasting pain relief to patients not responding to medical management. The choice of intervention will depend on the region involved, cost, and invasiveness. Simple procedures such as intercostal nerve blocks/neurolysis, stellate ganglion blocks, paravertebral neurolysis, epidural steroid injections, and dorsal root ganglion-radiofrequency ablation are effective interventions, and if they fail, spinal cord stimulators could be effective in the hands of experienced pain physicians.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".