Prevalence, Clinical Correlates, and Treatment of Migraine in Bipolar Disorder
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence, clinical correlates, and treatment of migraine in bipolar disorder. BACKGROUND: The relationship between migraine and mood disorders has been of long-standing interest to researchers and clinicians. Although a strong association has been demonstrated consistently for migraine and major depression, there has been less systematic research on the links between migraine and bipolar disorder. METHODS: A migraine questionnaire (based on International Headache Society criteria) was administered to 108 outpatients with bipolar disorder. Information on the clinical course of bipolar illness was also collected. RESULTS: The overall lifetime prevalence of migraine was 39.8% (43.8% among women and 31.4% among men). In the subgroup of patients with bipolar II disorder, the lifetime prevalence of migraine was 64.7%. The bipolar with migraine group was younger, tended to be more educated, was more likely to be employed or studying, and had fewer psychiatric hospitalizations. Their initial presentation for psychiatric treatment was more often for symptoms of depression, rather than hypomania or mania. They were more likely to have a family history of migraine and psychiatric disorders, and a greater number of affected relatives. They were less likely to use mood stabilizers, and more likely to use atypical antidepressants. Migraine was assessed by a neurologist in only 16% of affected patients. The prevalence of the use of specific antimigraine medications (triptans) was 27.9%. CONCLUSIONS: This study confirms the higher prevalence of migraine among those with bipolar disorder compared to the general population. Migraine in patients with bipolar disorder is underdiagnosed and undertreated. Bipolar disorder with migraine is associated with differences in the clinical course of bipolar disorder, and may represent a subtype of bipolar disorder.
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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 | 0.000 | 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) | 0.002 | 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".