Melatonin Compared to Other Treatments for Episodic Migraine: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Migraine is one of the most common neurological diseases, presenting different characteristics among patients. Therefore, there is a need to identify preventive medications that offer more efficacy and fewer adverse effects. Melatonin is a promising therapeutic alternative in this context due to its analgesic, neuromodulatory and cerebral blood flow regulatory mechanism. OBJECTIVE: This study aims to evaluate the efficacy of melatonin treatment compared to placebo and other drugs in reducing migraine episodes' frequency and secondary outcomes by analyzing randomized clinical trials. METHODS: The databases Cochrane, Embase and PubMed were used to search and select relevant studies, according to their specific inclusion criteria. Afterward, the relevant data was extracted, and statistical analysis was conducted with R Studio version 4.3.1, applying appropriate models to maintain heterogeneity within them and produce a combined estimate. Results were interpreted considering potential biases and limitations to form our final statement with the Risk of Bias (RoB 2.0) tool from Cochrane. RESULTS: A total of nine studies involving 783 patients were included in our analysis. Treatment methods were composed of seven different strategies. The network meta-analysis showed no statistically significant differences related to monthly headache frequency between melatonin and amitriptyline (SD: -1.8; 95% Crl [-5.2, 1.0]); naproxen (SD: -0.98; 95% Crl [-5.5, 3.8]); valproic acid (SD: -0.60; 95% Crl [-5., 3.6]); topiramate (SD: 0.081; 95% Crl [-5.0, 4.7]); propanolol (SD: 1.4; 95% Crl [-3.7, 6.6]) and placebo (SD: 0.49; 95% Crl [-1.6, 2.7]). Other outcomes assessed were the MIDAS score, the mean number of analgesics used and headache duration, in hours, all of which had nonsignificant differences among treatment arms. CONCLUSION: This systematic review and network meta-analysis found no substantial support for the efficacy of melatonin treatment in patients with episodic migraine, challenging the assumption of their correlation. Although the results showed no significant association between the disease and melatonin administration, more research is necessary to explore the influence of melatonin in migraine's pathophysiology and further potential indirect mechanisms by which melatonin usage could benefit those who have not responded to conventional therapies.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".