Factors associated with depression in patients referred to headache specialists
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between selected demographic characteristics and clinical features in patients with headache and depression. METHODS: We studied demographic and clinical data collected at the time of consultation for 712 new patients with headache referred to five headache specialty clinics in Canada. Data were analyzed as part of the Canadian Headache Outpatient Registry and Database (CHORD) Project. The Beck Depression Inventory (BDI-II) was used to identify the presence of depression. Multivariable logistic regression analysis was employed to evaluate associations between age, gender, employment status, marital status, diagnosis, headache days per month, medication overuse, headache impact (HIT-6), and headache disability (MIDAS) and the presence of depression as measured by the BDI-II. RESULTS: Among the sample of patients with headache, 27% (n = 189) had moderate to severe depression. Factors independently associated with depression included age less than 50 years, being unemployed, being on disability pension or welfare, being widowed, separated, or divorced, a diagnosis of transformed migraine or headache associated with head trauma or cervical spine disorder, and showing severe headache impact as measured by the HIT-6, or severe disability as measured by the MIDAS. CONCLUSIONS: In patients with headache referred for specialist consultation, depression is strongly associated with being on disability or welfare, unemployment, age under 50 years, showing severe headache impact on the Headache Impact Test-6, and receiving a diagnosis of transformed migraine. The possibility of a concomitant depression should be strongly considered in patients with headache with any of these characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".