Efficacy of Remote Electrical Neuromodulation (REN) on Migraine-Induced Neuronal Activity: A Systematic Review
Bibliographic record
Abstract
This systematic review evaluated the efficacy of Remote Electrical str Neuromodulation (REN) in migraine-induced neuronal activity. A PubMed and Google Scholar search identified studies published between 2007 and 2025. Inclusion criteria focused on randomized controlled trials, open-label studies, and real-world observational studies that evaluated REN for migraine treatment. Duplicate reports and studies with insufficient information were excluded. Data extraction and quality assessment followed preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and the Newcastle-Ottawa Scale. The results showed that 10 peer-reviewed studies were included (4 Randomized Controlled Trials [RCTs], 4 open-label studies, and 2 real-world observational studies). REN demonstrated significant improvements in pain freedom and pain relief at 2 hours compared to placebo (pain freedom: 41-46% vs. 20-25%, pain relief: 59-67% vs. 35-42%). Patients treated with REN also showed significant reductions in migraine attacks and improved daily functioning. REN was well-tolerated, with minimal side effects, primarily mild local paresthesia or skin sensitivity. The majority of RCTs had low risk of bias, though observational studies exhibited a higher risk. It was concluded that REN may serve as a valuable nonpharmacologic option for migraine management.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".