Associations between headache and stress, alcohol drinking, exercise, sleep, and comorbid health conditions in a Japanese population
Bibliographic record
Abstract
We conducted a cross-sectional survey of 12,988 subjects aged 20-79 years (5,908 men and 7,090 women) receiving health checkups at a Tokyo clinic. They filled out a self-administered structured questionnaire, and 5.4% of the men and 15.4% of the women reported having headaches. Younger subjects were more prone to having headaches. The likelihood of having headaches increased with stress level and decreased ability to relieve stress in both genders. There was an inverse dose-response relationship between having headaches and alcohol consumption, and less walking/exercise and sleep problems increased the likelihood of headaches in both genders. Headache sufferers of both genders were more likely to report multiple additional poor health conditions. A multivariate stepwise logistic analysis showed that age, self-estimated degree of stress, reported number of additional poor health conditions, and less alcohol consumption were independently correlated with having headaches. In conclusion, although women were more susceptible to headache, Japanese men and women in Tokyo shared factors associated with headache, including age, stress, having other poor health conditions, alcohol consumption, sleep, and exercise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".