European Headache Federation (EHF) critical re-appraisal and meta-analysis of oral drugs in migraine prevention—part 1: amitriptyline
Bibliographic record
Abstract
OBJECTIVE: The aim of this paper is to critically re-appraise the published trials assessing amitriptyline for migraine prophylaxis. METHODS: We report our methods and results following the Preferred Reporting Items for Systematic Reviews (PRISMA), by searching MEDLINE, EMBASE, Cochrane CENTRAL, and ClinicalTrials.gov for randomized trials of pharmacologic treatments for migraine prophylaxis. We included randomized trials that compared amitriptyline with placebo for migraine prophylaxis in adults. Our outcomes of interest were informed by the Outcome Set for preventive intervention trials in chronic and episodic migraine (COSMIG) and include the proportion of patients who experience a 50% or more reduction in migraine days per month, migraine days per month, and adverse events leading to discontinuation. We assessed risk of bias by using a modified Cochrane RoB 2.0 tool and the certainty of evidence by using the GRADE approach. RESULTS: Our search yielded 10.826 unique records, of which three trials (n = 622) were eligible for data synthesis and analysis. We found moderate certainty evidence that amitriptyline increases the proportion of patients who experience a 50% or more reduction in monthly migraine days, compared to placebo (relative risk: 1.60 (95% CI 1.17 to 2.19); absolute risk difference: 165 more per 1,000 (95% CI 47 more to 327 more). We found moderate certainty evidence that amitriptyline increases the proportion of patients who discontinue due to adverse events compared to placebo (risk difference: 0.05 (95% CI 0.01 to 0.10); absolute risk difference: 50 more per 1,000 (95% CI 10 more to 100 more). CONCLUSIONS: Our meta-analysis showed that amitriptyline may have a prophylactic role in migraine patients, however these results are far from robust. This warrants further large-scale research to evaluate the role of amitriptyline in migraine prevention.
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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 | 0.184 | 0.358 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".