Topical analgesics for neuropathic pain: an evidence-informed guide for the practicing clinician
Bibliographic record
Abstract
OBJECTIVE: To evaluate available evidence for the efficacy and safety of topical analgesics for neuropathic pain and to offer treatment guidance. METHODS: An expert panel searched PubMed (Medline) and reference lists of published articles for available literature assessing 8 categories of topical analgesics used to treat various neuropathic pain conditions. The panel rated the level of analgesic efficacy evidence for each treatment and considered safety, ease of use, and cost. The degree of consensus on the recommendations among the panelists was measured. RESULTS: There was strong evidence and high consensus that capsaicin 8% is effective for diabetic peripheral neuropathy and postherpetic neuralgia and that lidocaine is effective for postherpetic neuralgia. There was strong evidence and moderate consensus that capsaicin 8% could be effective for HIV-induced neuropathy. There was moderate evidence and high consensus that lidocaine is likely effective for diabetic peripheral neuropathy, idiopathic neuropathy, and postsurgical neuropathy and that capsaicin 8% might be effective for chemotherapy-induced peripheral neuropathy and complex regional pain syndrome. Evidence was weak for other topical medications, though the panel strongly agreed that antidepressants might help with postherpetic neuralgia, complex regional pain syndrome, postsurgical neuropathy, and post-traumatic neuropathy; that nonsteroidal anti-inflammatory drugs could help with postsurgical neuropathy; and that gabapentin might benefit vulvodynia. There was less agreement about whether antidepressants might benefit diabetic peripheral neuropathy, chemotherapy-induced peripheral neuropathy, and vulvodynia and whether capsaicin 8% could be effective for postsurgical neuropathy. CONCLUSIONS: Recommendations were based on a survey and grading of existing literature and, when strong evidence was lacking, the collective clinical expertise of panelists.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".