Chronic Migraine Epidemiology and Outcomes – International (CaMEO-I) Study: Methods and multi-country baseline findings for diagnosis rates and care
Bibliographic record
Abstract
Background The Chronic Migraine Epidemiology and Outcomes-International study provides insight into people with migraine in multiple countries. Methods This cross-sectional, observational, web-based cohort study was conducted in Canada, France, Germany, Japan, United Kingdom, and United States. An initial Screening Module survey solicited general healthcare information from a representative sample and identified participants with migraine based on modified International Classification of Headache Disorders-3 criteria; those with migraine completed a detailed survey based on validated migraine-specific assessments. Results Among 90,613 people who correctly completed the screening surveys, 76,121 respondents did not meet the criteria for migraine, while 14,492 did. Among respondents with migraine, mean age ranged from 40 to 42 years. The median number of monthly headache days ranged from 2.33 to 3.33 across countries, while the proportion of respondents with moderate-to-severe disability (measured by Migraine Disability Assessment) ranged from 30% (Japan) to 52% (Germany). The proportion of respondents with ≥15 monthly headache days ranged from 5.4% (France) to 9.5% (Japan). Fewer than half of respondents with migraine in each country reported having received a migraine diagnosis. Conclusion These results demonstrated high rates of migraine-related disability and underdiagnosis of migraine across six countries. This study will characterize country-level burden, treatment patterns, and geographical differences in care.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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; 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".