Alcohol Use Disorder in Patients with Chronic Migraine: A Retrospective, Observational Study
Bibliographic record
Abstract
ABSTRACT: Objective: The relationship between migraine and alcohol consumption is unclear. We assessed the association between chronic migraine and alcohol use disorder(AUD), relative to chronic disease controls, and in conjunction with common comorbidities. Methods: We conducted a retrospective, observational study. The primary outcome was the odds ratio for AUD in patients with chronic migraine or with chronic migraine and additional comorbidities relative to controls. Results: A total of 3701 patients with chronic migraine, 4450 patients with low back pain, and 1780 patients with type 2 diabetes mellitus met inclusion criteria. Patients with chronic migraine had a lower risk of AUD relative to both controls of low back pain (OR 0.37; 95% CI: 0.29–0.47, p < 0.001) and type 2 diabetes mellitus (OR 0.39; 95% CI: 0.29–0.52, p < 0.001). Depression was associated with the largest OR for AUD in chronic migraine (OR 8.62; 95% CI: 4.99–14.88, p < 0.001), followed by post-traumatic stress disorder (OR 6.63; 95% CI: 4.13–10.64, p < 0.001) and anxiety (OR 3.58; 95% CI: 2.23–5.75, p < 0.001). Conclusion: Patients with chronic migraine had a lower odds ratio of AUD relative to controls. But in patients with chronic migraine, those with comorbid depression, anxiety, or PTSD are at higher risk of AUD. When patients establish care, comorbid factors should be assessed and for those at higher risk, AUD should be screened for at every visit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".