Clinical Features and Pharmacological Treatment of Migraine Patients Referred to Headache Specialists in Canada
Bibliographic record
Abstract
We set out to examine selected clinical characteristics of migraine patients referred to neurologists specializing in headache in Canada, and to document their pharmacological therapy both before and after consultation with the neurologist. Demographic, clinical and pharmacotherapy data were collected at the time of consultation for 606 patients referred to five headache clinics and who were given a migraine diagnosis by the neurologist. Data were analysed as part of the Canadian Headache Outpatient Registry and Database (CHORD) Project. The mean age of the migraine patients was 39.7 years; and 82.5% were female. The majority of patients suffered severe impact from their headaches. Prior to consultation, 48.7% were taking a triptan; after consultation, 97.2% were on a triptan. Before consultation, 30.9% were on a prophylactic drug; after consultation, 70.4% were. 20.8% of patients were medication overusers. Of these medication overusers, 42.4% were overusing an opiate, usually in combination with other analgesics; 21.6% were overusing a triptan. Medication changes made by the neurologists at consultation included a large increase in the use of both triptans and prophylactic medications. Medication overuse, particularly opiate overuse, remains a significant problem in patients with migraine 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".