Prevalence of Reported Migraine Headaches in Canadian Adolescents
Bibliographic record
Abstract
OBJECTIVE: To examine self-reported prevalence data for migraine among adolescent Canadians and to explore how reported migraine treatment varies by age. METHODS: We analyzed the microdata files of the Canadian National Population Health Survey (1996-1997). Respondents reported whether they had "migraine headaches diagnosed by a health professional". They also reported whether they received "any treatment or medication for migraine headaches", with treatments subdivided into drug, diet or "other". RESULTS: 99.9% of 173,216 eligible respondents reported whether they had migraine headaches. Migraine was reported by 2.4% of Canadian youth aged 12-14 years and by 5.0% of 15-19 year-olds compared to 7.2% of adults aged > or = 20 years of age (p< 0.0001, chi-square). Active treatment was used by 51.0% - higher by females (53.1%) than males (44.7%) (p<0.0001 chi-square). Treatment was used by 45.1% of 12-14 year-olds, by 45.7% of 15-19 year-olds and by 51.5% of those > or = 20 years (p=0.0027). The nature of the active treatment choice (drug, diet or other) did not significantly vary within the age groups studied. CONCLUSIONS: We present robust estimates of self-report diagnosed migraine prevalence, derived from a large nationally representative population survey. Estimates of the prevalence of active treatment for migraine provide insight into the burden of migraine within this population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".