An Assessment of Sex and Gender Considerations in Migraine Calcitonin Gene-Related Peptide Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Published guidelines for conducting clinical trials for migraine therapeutics recommend recruiting participants based on disease epidemiology and including sex/gender-based subpopulation analyses. These recommendations aim to improve the quality and generalizability of migraine clinical trials. The aim of this study was to summarize participant demographics in migraine clinical trials for FDA-approved calcitonin gene-related peptide (CGRP)-targeting drugs (receptor antagonists [gepants], CGRP peptide or receptor monoclonal antibodies [mAbs]) and assess the use of sex/gender-based subpopulation analyses in these studies. METHODS: We conducted a review of industry-sponsored migraine clinical trials for FDA-approved CGRP-targeting medications. Demographic data (sex and/or gender) from phase II or III trials were abstracted, and the use of sex/gender-based analyses was recorded. RESULTS: Fourteen trials of gepants were included in this analysis. Participants who were identified as females or women were more likely to participate in these trials (87.0 ± 2.2%). Twenty-four trials of CGRP mAbs were reviewed. These studies also reported that participants were predominantly identified as female or women (84.9 ± 2.3%). None of the clinical trials reviewed reported sex/gender-based analyses of their results. CONCLUSIONS: This study suggests that men are underrepresented in migraine CGRP clinical trials. Greater attention to sex and gender is needed in migraine clinical trial design so that they better align with current recommendations made by headache societies and regulatory agencies.
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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 | 0.390 | 0.495 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".