Disability in migraine: Multicountry results from the Chronic Migraine Epidemiology and Outcomes – International (CaMEO-I) Study
Bibliographic record
Abstract
BACKGROUND: Few studies of migraine have evaluated migraine disability across multiple countries using the same methodology. METHODS: This cross-sectional, web-based survey was conducted in 2021-2022 in Canada, France, Germany, Japan, UK and USA. Respondents with migraine were identified based on modified International Classification of Headache Disorders, 3rd edition, criteria. Headache features (Migraine Symptom Severity Score (MSSS, range: 0-21), presence of allodynia (Allodynia Symptom Checklist, ASC-12)) and migraine burden (Patient Health Questionnaire-4 (PHQ-4), Migraine-Specific Quality of Life questionnaire version 2.1 (MSQ v2.1), Work Productivity and Activity Impairment (WPAI) questionnaire) were evaluated. RESULTS: Among 14,492 respondents with migraine across countries, the mean ± SD MSSS was 15.4 ± 3.2 and 48.5% (7026/14,492) of respondents had allodynia based on ASC-12. Of all respondents living with migraine, 35.5% (5146/14,492) reported moderate to severe anxiety and/or depression symptoms. Mean ± SD MSQ v2.1 Role Function-Restrictive, Role Function-Preventive and Emotional Function domain scores were 60.7 ± 22.9, 71.5 ± 23.0 and 65.1 ± 27.2, respectively. The WPAI mean ± SD percentages of respondents who missed work or worked impaired as a result of migraine were 6.8 ± 18.1% and 41.0 ± 30.1%, respectively. CONCLUSIONS: For every country surveyed, migraine was associated with high levels of symptom severity, with allodynia and with substantial burden.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".