Melatonin for neuropathic pain: a double-blind, placebo-controlled, randomized, crossover trial
Bibliographic record
Abstract
ABSTRACT: Neuropathic pain (NP) is a common challenging problem, and there is a growing need to develop safe and effective nonopioid treatments. Sleep disturbance is commonly associated with NP because pain intensity of NP conditions is often worse at night. Some evidence suggests that the pineal hormone, melatonin, may reduce pain in clinical settings. We conducted a clinical trial to evaluate the efficacy of melatonin for NP. Using a double-blind, placebo-controlled, crossover design, 31 adults with NP were randomly allocated to 1 of 2 sequences of treatment with melatonin and placebo. During each of 2 treatment periods, participants took capsules containing melatonin or placebo for 4 weeks, followed by a 7-day washout period. The primary outcome was mean daily pain intensity (0-10) at maximally tolerated doses (MTD) during each period. Secondary outcomes, assessed at MTD, included adverse events, and measures of sleep, mood, and quality of life. Thirty-one participants were recruited, and 30 participants completed both treatment periods of the trial. The mean maximal tolerated dose of melatonin in this trial was 11.9 mg/day. Treatment-emergent adverse events with melatonin were infrequent and not statistically different from placebo. At MTD, mean daily pain (standard error) was 4.1 (0.3) for melatonin and 4.2 (0.3) for placebo ( P = 0.8). There were no statistically significant differences between placebo and melatonin for any secondary outcomes. Overall, the results of this trial do not provide any evidence to suggest promise for melatonin as an effective treatment for NP.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".