European Headache Federation (EHF) critical re-appraisal and meta-analysis of oral drugs in migraine prevention—part 2: flunarizine
Bibliographic record
Abstract
Abstract Objective Novel disease-specific and mechanism-based treatments sharing good evidence of efficacy for migraine have been recently marketed. However, reimbursement by insurers depends on treatment failure with classic anti-migraine drugs. In this systematic review and meta-analysis, we aimed to identify and rate the evidence for efficacy of flunarizine, a repurposed, first- or second-line treatment for migraine prophylaxis. Methods A systematic search in MEDLINE, Cochrane CENTRAL, and ClinicalTrials.gov was performed for trials of pharmacological treatment in migraine prophylaxis, following the Preferred Reporting Items for Systematic Reviews (PRISMA). Eligible trials for meta-analysis were randomized, placebo–controlled studies comparing flunarizine with placebo. Outcomes of interest according to the Outcome Set for preventive intervention trials in chronic and episodic migraine (COSMIG) were the proportion of patients reaching a 50% or more reduction in monthly migraine days, the change in monthly migraine days (MMDs), and Adverse Events (AEs) leading to discontinuation. Results Five trials were eligible for narrative description and three for data synthesis and analysis. No studies reported the predefined outcomes, but one study assessed the 50% reduction in monthly migraine attacks with flunarizine as compared to placebo showing a benefit from flunarizine with a low or probably low risk of bias. We found that flunarizine may increase the proportion of patients who discontinue due to adverse events compared to placebo (risk difference: 0.02; 95% CI -0.03 to 0.06). Conclusions Published flunarizine trials predate the recommended endpoints for evaluating migraine prophylaxis drugs, hence the lack of an adequate assessment for these endpoints. Further, modern-day, large‐scale studies would be valuable in re-evaluating the efficacy of flunarizine for the treatment of migraines, offering additional insights into its potential benefits.
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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 | 0.109 | 0.183 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.037 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".