The comparative effectiveness of migraine preventive drugs: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: While there are several trials that support the efficacy of various drugs for migraine prophylaxis against placebo, there is limited evidence addressing the comparative safety and efficacy of these drugs. We conducted a systematic review and network meta-analysis to facilitate comparison between drugs for migraine prophylaxis. METHODS: We searched MEDLINE, EMBASE, CENTRAL, and clinicaltrials.gov from inception to August 13, 2022, for randomized trials of pharmacological treatments for migraine prophylaxis in adults. Reviewers worked independently and in duplicate to screen references, extract data, and assess risk of bias. We performed a frequentist random-effects network meta-analysis and rated the certainty (quality) of evidence as either high, moderate, low, or very low using the GRADE approach. RESULTS: We identified 74 eligible trials, reporting on 32,990 patients. We found high certainty evidence that monoclonal antibodies acting on the calcitonin gene related peptide or its receptor (CGRP(r)mAbs), gepants, and topiramate increase the proportion of patients who experience a 50% or more reduction in monthly migraine days, compared to placebo. We found moderate certainty evidence that beta-blockers, valproate, and amitriptyline increase the proportion of patients who experience a 50% or more reduction in monthly migraine days, and low certainty evidence that gabapentin may not be different from placebo. We found high certainty evidence that, compared to placebo, valproate and amitriptyline lead to substantial adverse events leading to discontinuation, moderate certainty evidence that topiramate, beta-blockers, and gabapentin increase adverse events leading to discontinuation, and moderate to high certainty evidence that (CGRP(r)mAbs) and gepants do not increase adverse events. CONCLUSIONS: (CGRP(r)mAbs) have the best safety and efficacy profile of all drugs for migraine prophylaxis, followed closely by gepants.
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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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".