Transcutaneous Electrical Nerve Stimulation for Prevention and Treatment of Post-Herpetic Neuralgia: A Narrative Review
Bibliographic record
Abstract
The present investigation evaluated transcutaneous electrical nerve stimulation (TENS) for the non-pharmacological treatment of post-herpetic neuralgia (PHN). PHN is the most common complication of the Herpes Zoster virus, affecting one in every five patients with shingles, and presents as intense neuropathic pain that can persist for 90 days or longer after the initial onset of symptoms. Current pharmacological treatment options are mainly limited to symptom management, including oral medications such as tricyclic antidepressants and gabapentinoid preparations, as well as topical options such as lidocaine or capsaicin. TENS is a minimally invasive, non-pharmacological electrical nerve stimulation device currently approved for the treatment of neuropathic pain in adults, providing patients with an alternative or adjunct treatment option to medication with a reduced potential for unwanted side effects, drug-drug interactions or potentially life-threatening toxicity. Current indications for the use of TENS in the treatment of PHN are recommended only after therapeutic goals are attempted and unsuccessfully met by current mainstay medications. More research into the efficacy and safety of TENS for treating PHN should be conducted to clarify further its role as a mainstay treatment for this condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".