Association between Body Mass Index and Migraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it