Prevalence and Determinants of Sinus Problems in Farm and Non-Farm Populations of Rural Saskatchewan, Canada
Bibliographic record
Abstract
Although sinus problems have long been recognized as the most common respiratory symptoms associated with agricultural work, there is a scarcity of recent studies and/or reliable estimates as to the true prevalence or risk factors of sinus problems related to farming. The aim of this study was to determine the prevalence of sinus problems in farming and non-farming rural populations and further investigate the association of individual (for example life-style, occupational), contextual (e.g., environmental), and important covariates (e.g., age, sex) with sinus problems. A large-scale cross-sectional study was conducted in farm and non-farm residents of rural Saskatchewan, Canada. A logistic regression model based on a generalized estimating equations approach were fitted to investigate the risk factors of sinus problems. Sinus problems were reported by 2755 (34.0%) of the 8101 subjects. Farm residents were more likely to spend their first year of life on farm compared with non-farm residents, and indicated a significantly lower risk of sinus problems. Meanwhile, occupational exposure to solvent and mold were associated with an increased risk of sinus problems. Some health conditions such as allergy and stomach acidity/reflux, family history, and female sex were also related to a higher risk of sinus problems. Farm residents had a significantly lower risk of sinus problems than non-farm residents, likely due to the exposure to farm specific environments in their early life.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".