Melatonin for Neuropathic Pain: Protocol for a Double-blind, Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Neuropathic pain (NP), a complication of several conditions (eg, diabetic neuropathy and varicella zoster), 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 in NP conditions is often worse at night. The pineal hormone melatonin has been shown to reduce pain in both preclinical and clinical settings, in addition to multiple trials demonstrating efficacy for primary insomnia and delayed sleep phase syndrome. OBJECTIVE: We propose to conduct a clinical trial to evaluate the efficacy and safety of melatonin for NP. METHODS: Using a double-blind, placebo-controlled, crossover design, 30 adults with NP will be randomly allocated to one of two sequences of treatment with melatonin and placebo. During each of the two treatment periods, participants will take capsules containing melatonin or placebo for 4 weeks, followed by a 7-day washout period. The primary outcome will be mean daily pain intensity (scored 0-10) at maximally tolerated doses (MTDs) during each period. Secondary outcomes, assessed at MTDs, will include global improvement, adverse events, mood, and quality of life. RESULTS: This trial was registered in the International Standard Randomized Controlled Trial registry May 4, 2022 (ISRCTN #16215617), attained conditional ethics approval May 9, 2022 (Queen's University Health Sciences & Affiliated Teaching Hospitals Research Ethics Board protocol number ANAE-387-22), and recruitment is set to start August 2022. CONCLUSIONS: This trial will provide rigorous evidence comparing the efficacy of melatonin to that of placebo in the treatment of NP. TRIAL REGISTRATION: ISRCTN Registry 16215617; https://www.isrctn.com/ISRCTN16215617. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40025.
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.102 | 0.015 |
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".