Migraine Prevalence, Treatment and Impact: The Canadian Women and Migraine Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of migraine headache varies somewhat across geographic regions. The last Canadian population-based study of migraine was in 1994. We report the findings of the Canadian Women and Migraine Survey. In addition to reporting migraine prevalence in Canadian women, the survey identified current consultation and treatment practices of women with migraine, and the psychological burden of migraine. METHODS: The survey was conducted with a population-based sample of 1210 women using standard telephone research methods. Headache diagnoses were based on the International Headache Society (IHS) Classification. RESULTS: Calculated prevalence of migraine headache was 26%. Only 51% of women with migraine had consulted a physician about their headaches. Women with migraines rely on over-the-counter medications and non-specific prescription medications. Less than 10% of women with migraine use triptans/dihydroergotamine for primary treatment. Ninety seven percent of women with migraine reported at least one psychosocial impact resulting from migraines. CONCLUSIONS: The prevalence of migraine in Canadian women appears static, and is again shown to be slightly higher than that reported in the United States. As in other epidemiologic studies, many women with migraine do not seek medical help for their headaches and perhaps as a result, few are using migraine-specific medications to treat their headaches. The impact of migraine on Canadian women is substantial with almost all women with migraine reporting adverse psychosocial effects of migraines on their lives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".