Bibliographic record
Abstract
OBJECTIVES: This article provides prevalence and incidence estimates of migraine among Canadians aged 12 or older. Associations with selected socio-demographic factors and health characteristics are also examined. Selected health indicators and medication use, as well as health care use and attitudes, are discussed, comparing migraineurs with non-migraineurs. DATA SOURCES: The findings are based on the cross-sectional and longitudinal household components of the first three cycles (1994/95, 1996/97 and 1998/99) of Statistics Canada's National Population Health Survey. Information on hospital stays is from the 1997/98 Hospital Morbidity Database, maintained by the Canadian Institute for Health Information. ANALYTICAL TECHNIQUES: Cross-tabulations were used to estimate the prevalence and incidence of migraine. Associations of migraine with selected factors were examined using generalized logistic regression. MAIN RESULTS: In 1998/99, migraine was most prevalent among women, 25- to 54-year-olds, Whites, and individuals in low-income households. The odds of being diagnosed with migraine were higher for women with pre-existing sinusitis, bronchitis or emphysema, compared with women without these conditions. The odds of this disorder for men were associated with previously diagnosed arthritis or rheumatism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.158 | 0.054 |
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".