Occupational and environmental risk factors for respiratory symptoms in rural Beijing, China
Bibliographic record
Abstract
The aim of the present study was to determine the effects of occupational and environmental exposure on respiratory symptoms in adults in rural Beijing, China. Thirty randomly selected villages in the counties of Shunyi and Tongxian, 50 km north and east, respectively, of the city of Beijing, China, participated in this study. Village doctors interviewed all residents aged > or = 15 yrs and completed the International Union Against Tuberculosis and Lung Disease Questionnaire on Bronchial Symptoms translated into Chinese with added questions on smoking and occupational and environmental exposure. Of the eligible population, 22,528 adults (98%) took part. The prevalence of all respiratory symptoms, i.e. asthma-like symptoms, asthma attacks in the last 12 months, chronic cough and chronic phlegm, was low. Significant determinants for respiratory symptoms were age, sex, smoking and county of residence. A dose-dependent relationship was found between cumulative cigarette consumption and prevalence of respiratory symptoms. After adjusting for these variables, exposure to insecticides and fertilisers significantly increased the risk of most of the respiratory symptoms, whereas exposure to indoor air pollution from domestic fuels did not. Exposure to chemicals such as insecticides and fertilisers contributed independently to the risk of respiratory symptoms in rural Beijing, China.
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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 | 0.000 | 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".