Does Exposure to Agricultural Chemicals Increase the Risk of Prostate Cancer among Farmers?
Bibliographic record
Abstract
Several studies suggest that farmers may be at increased risk of prostate cancer. The present analysis, based on a large population-based case-control study conducted among men in the Montreal area in the early 1980's, aim at identifying occupational chemicals which may be responsible for such increases. The original study enrolled 449 prostate cancer cases, nearly 4,000 patients with other cancers, as well as 533 population controls. Subjects were interviewed about their occupation histories, and a team of industrial hygienists assigned their past exposures using a checklist of some 300 chemicals. The present analysis was restricted to a study base of men who had worked as farmers earlier in their lives. There were a total of 49 men with prostate cancers, 127 with other cancers and 56 population controls. We created a pool of 183 controls combining the patients with cancers at sites other than the prostate and the population controls. We then estimated the odds ratio for prostate cancer associated with exposure to each of 10 agricultural chemicals, i.e., pesticides, arsenic compounds, acetic acid, gasoline engine emissions, diesel engine emissions, polycyclic aromatic hydrocarbons from petroleum, lubricating oils and greases, alkanes with >or=18 carbons, solvents, and mononuclear aromatic hydrocarbons. Based on a model adjusting for age, ethnicity, education, and respondent status, there was evidence of a two-fold excess risk of prostate cancer among farmers with substantial exposure to pesticides [odds ratio (OR)=2.3, 95% confidence interval (CI) 1.1-5.1], as compared to unexposed farmers. There was some suggestion, based on few subjects, of increased risks among farmers ever exposed to diesel engine emissions (OR=5.7, 95% CI 1.2-26.5). The results for pesticides are particularly noteworthy in the light of findings from previous studies. Suggestions of trends for elevated risks were noted with other agricultural chemicals, but these are largely novel and need further confirmation in larger samples.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".