Demographics and Clinical Features of Patients Referred to Headache Specialists
Bibliographic record
Abstract
OBJECTIVE: To examine demographic characteristics and clinical features of headache patients referred to neurologists specializing in headache in Canada. METHODS: Demographic and clinical data were collected at the time of consultation for 865 new headache patients referred to five headache-specialty clinics in Canada. The Headache Impact Test (HIT-6) and Migraine Disability Questionnaire (MIDAS) were used to measure headache impact and disability. Data were analyzed as part of the Canadian Headache Outpatient Registry and Database (CHORD) Project. RESULTS: The average age of the patients was 40 years and the majority were female (78%). Most were employed either full time (49%) or part time (13%). The majority of patients were diagnosed with either migraine or tension-type headache (78%). Over a third of patients experienced headache every day, and half had experienced a headache in the previous month which was of severe intensity. Most (80%) scored in the "very severe" category of the HIT-6 and over half (55%) were severely disabled as measured by the MIDAS. CONCLUSION: Patients referred to headache specialists in Canada are severely disabled by their headache disorders. These patients are in the most productive phase of their lives in terms of age and employment. It is important to provide the best available treatment to headache patients in order to minimize the disability and impact of their headache disorders.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".