Comorbidity of Major Depression and Migraine — A Canadian Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of major depressive episodes (MDEs) in patients with migraine and to compare the strength of association with that of other long-term medical conditions. METHODS: This study used a large-scale probability sample (over 130,000 sample) from the Canadian Community Health Survey (CCHS), a cross-sectional survey conducted by Statistics Canada. The CCHS screened for a broad set of medical conditions. Major depression was evaluated with the Composite International Diagnostic Interview Short Form for Major Depression, and the diagnosis of migraine was self-reported. The annual prevalence of major depression was calculated in the general population, in subjects with migraine, and in those with chronic conditions other than migraine. RESULTS: The prevalence of major depression in subjects reporting migraine was higher than that in the general population or in subjects with other chronic medical conditions (17.6%, compared with 7.4% and 7.8%, respectively). CONCLUSIONS: There is a strong association between major depression and migraine. The migraine-MDE association may account for a large fraction of the chronic condition-MDE association. The association between migraines and MDE differs from that of other chronic conditions, as the association persists into older age groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".