Association between Body Mass Index and Migraine
Bibliographic record
Abstract
OBJECTIVE: To explore the prevalence of overweight and obesity in patients with migraine. BACKGROUND: Previous studies support the concept that obesity is an exacerbating factor for migraine. Also, some studies have found an increased frequency of obesity and overweight in migraine patients compared to the normal population. METHODS: We studied 1,371 patients with migraine and 612 controls. The migraine population was matched by gender with a healthy control group. RESULTS: Mean age of patients with migraine was 38.0 +/- 13.3 years and in the controls it was 34.8 +/- 12.1 years. The percentage of females in both groups was similar (migraine 81.6% vs. control 83.3%, p = 0.40). The distribution of body mass index (BMI) in migraine patients and controls was as follows: underweight patients (BMI <18.5) 3.1% migraine versus controls 1.5%; normal (BMI 18.5-24.9) 44.8% migraine versus controls 47.1%; overweight (BMI 25-29.9) 38.3% migraine versus controls 33.7%; obese (BMI 30-34.5) 10.3% migraine versus controls 13.6%; morbidly obese (BMI 35) 3.4% migraine versus controls 4.2%. Overweight and obesity in migraine patients versus controls were statistically significant. No association was found between the disability and severity of migraine and BMI. CONCLUSIONS: This study did not find associations between severity or disability of migraine and BMI.
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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) | 0.003 | 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".