Prevalence and Environmental Triggers of Migraine Headache Among Petroleum Industry Workers in Southern Iran: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Migraine is one of the most prevalent types of headache worldwide, imposing a considerable financial and health burden. This study aimed to assess the prevalence of migraine and its environmental risk factors among workers inindustrial settings.Methods: This cross-sectional study was conducted from March to May 2022 among male workers at the South Pars Gas Complex in Iran. Participants were randomly recruited to complete a questionnaire that included demographic data, a standardized migraine diagnostic tool (based on the ICHD-3), and predisposing factors. Environmental and medical factors previously identified as potential triggers of migraine were examined in detail.Results: The overall prevalence of migraine was 14.9% (95% CI: 12.5–17.5; n=119/801), and probable migraine was 2.4% (95% CI: 1.4–3.7; n=19/801). Only a quarter of affected workers had been previously diagnosed or evaluated for migraine. Significant predictors of migraine included altitude difference between residence and workplace, poor sleep quality, and smoking. The most frequently reported triggers were weather (34.6% hot, 17.6% cold), sleep disturbances (27.5%), chemical odors (19.6%), stress (18.8%), and noise (7.2%). The most commonly used analgesic among participants was acetaminophen.Conclusion: Migraine appears to be more prevalent among industrial workers compared to the general population, with multiple environmental factors contributing to its occurrence.
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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 | 0.000 | 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) | 0.001 | 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".