Characterizing barriers to care in migraine: multicountry results from the Chronic Migraine Epidemiology and Outcomes – International (CaMEO-I) study
Bibliographic record
Abstract
OBJECTIVE: To assess rates of traversing barriers to care to access optimal clinical outcomes in people with migraine internationally. BACKGROUND: People in need of medical care for migraine should consult a health care professional knowledgeable in migraine management, obtain an accurate diagnosis, and receive an individualized treatment plan, which includes scientific society guideline-recommended treatments where appropriate. METHODS: The Chronic Migraine Epidemiology and Outcomes-International (CaMEO-I) Study was a cross-sectional, web-based survey conducted from July 2021 through March 2022 in Canada, France, Germany, Japan, the United Kingdom, and the United States (US). Respondents who met modified International Classification of Headache Disorders, 3rd edition, criteria for migraine and had Migraine Disability Assessment Scale (MIDAS) scores of ≥ 6 (i.e., mild, moderate, or severe disability) were deemed to need medical care and were included in this analysis. Minimally effective treatment required that participants were currently consulting a health care professional for headache (barrier 1), reported an accurate diagnosis (barrier 2), and reported use of minimally appropriate pharmacologic treatment (barrier 3; based on American Headache Society 2021 Consensus Statement recommendations). Proportions of respondents who successfully traversed each barrier were calculated, and chi-square tests were used to assess overall difference among countries. RESULTS: Among 14,492 respondents with migraine, 8,330 had MIDAS scores of ≥ 6, were deemed in need of medical care, and were included in this analysis. Current headache consultation was reported by 35.1% (2926/8330) of respondents. Compared with the US, consultation rates and diagnosis rates were statistically significantly lower in all other countries except France where they were statistically significantly higher. Total appropriate treatment rates were also statistically significantly lower in all other countries compared with the US except France, which did not differ from the US. All 3 barriers were traversed by only 11.5% (955/8330) of respondents, with differences among countries (P < 0.001). CONCLUSIONS: Of people with migraine in need of medical care for migraine, less than 15% traverse all 3 barriers to care. Although rates of consultation, diagnosis, and treatment differed among countries, improvements are needed in all countries studied to reduce the global burden of migraine. TRIAL REGISTRATION: NA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".