Characteristics of Adults with Migraine in Alberta, Canada: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Migraine, including episodic migraine (EM) and chronic migraine (CM), is a common neurological disorder that imparts a substantial health burden. OBJECTIVE: Understand the characteristics and treatment of EM and CM from a population-based perspective. METHODS: This retrospective population-based cross-sectional study utilized administrative data from Alberta. Among those with a migraine diagnostic code, CM and EM were identified by an algorithm and through exclusion, respectively; characteristics and migraine medication use were examined with descriptive statistics. RESULTS: From 79,076 adults with a migraine diagnostic code, 12,700 met the criteria for CM and 54,686 were considered to have EM. The majority of migraineurs were female, the most common comorbidity was depression, and individuals with CM had more comorbidities than EM. A larger proportion of individuals with CM versus EM were dispensed acute (80.6%: CM; 63.4%: EM) and preventative (58.0%: CM; 28.9%: EM) migraine medications over 1 year. Among those with a dispensation, individuals with CM had more acute (13.6 ± 32.2 vs. 4.6 ± 10.9 [mean ± standard deviation], 95% confidence interval [CI] 7.7-8.3), and preventative (12.6 ± 43.5 vs. 5.0 ± 12.6, 95% CI 6.9-8.4) migraine medication dispensations than EM, over 1-year. Opioids were commonly used in both groups (proportion of individuals dispensed an opioid over 1-year: 53.1%: CM; 25.7%: EM). CONCLUSIONS: Individuals with EM and CM displayed characteristics and medication use patterns consistent with other reports. Application of this algorithm for CM may be a useful and efficient means of identifying subgroups of migraine using routinely collected health data in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".