Chronic migraine epidemiology and outcomes – International (CaMEO-I) study: findings for diagnosis rates and care
Bibliographic record
Abstract
Objective To describe findings on migraine diagnosis, consulting, and current medication use for migraine across 6 countries. Methods CaMEO-I was a cross-sectional, observational, web-based study in 2021 conducted in: US, Canada, UK, Germany, France, and Japan. Qualified respondents provided sociodemographic back- ground, headache features, migraine disability using Migraine Disability Assessment Scale (MIDAS), history of consulting, diagnosis, and treatment patterns. Results A total of 14,492 individuals met criteria for migraine (~2400 per country) and were included in this analysis. Median monthly headache days (MHDs) ranged from 2.3 to 3.3 days, with between 5.4% (France) to 9.5% (Japan) of respondents reporting ≥15 MHDs. Moderate-to-severe migraine-related disa- bility was reported between 30.3% (Japan) to 52.0% (Germany). Self-reported medical diagnosis (SRMD) rates for migraine, chronic/transformed migraine, or menstrual migraine among those meeting ICHD-3 criteria ranged from 42.8% (Japan) to 49.3% (US). Overall migraine population rates of current preventive use ranged from 6.4% (Japan) to 16.8% (US). Conclusions Between one-third and one-half of respondents who met mICHD-3 criteria reported mod- erate-severe migraine-related disability as measured by MIDAS. While there were between-country differences in the proportion of CaMEO-I respondents with an SRMD of migraine and chronic migraine, underdiagnosis of migraine was a concern in each country studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".