0.025% Capsaicin Gel for the Treatment of Painful Diabetic Neuropathy: A Randomized, Double‐Blind, Crossover, Placebo‐Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Topical therapy may provide additional benefit in patients with painful diabetic neuropathy (PDN). This study was conducted to study the safety and efficacy of 0.025% capsaicin gel in this condition. METHODS: A 20-week, double-blind, crossover, randomized, single-center study enrolled subjects with PDN. They received 0.025% capsaicin gel or placebo for 8 weeks, with a washout period of 4 weeks between the two treatments. Primary efficacy end point was percent change in visual analog scale (0-100 mm) of pain severity. Secondary outcomes were score change in Neuropathic Pain Scale (NPS), short-form McGill Pain Questionnaires (SF-MPQ), proportion of patients who had pain score reductions of 30% and 50%, and adverse event. RESULTS: Of the 35 subjects screened, 33 were enrolled and 33 completed at least an 8-week treatment period. Intention-to-treat analysis showed no significant improvement in pain with capsaicin gel, compared with placebo with visual analog scale (VAS) score 28.8 mm vs. 34.6 mm (P = 0.53). No significant difference between the groups was found in SF-MPQ (7.4 vs. 7.71, P = 0.95), NPS (29.4 vs. 31.3, P = 0.81), and proportion of patients who had 30% or 50% pain relief. Capsaicin gel was well tolerated with minor skin reaction. CONCLUSIONS: 0.025% capsaicin gel is safe and well tolerated, but does not provide significant pain relief in patients with PDN.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".