Occupation related pesticide exposure and cancer of the prostate: a meta-analysis
Bibliographic record
Abstract
AIMS: To summarise recent literature on the risk of prostate cancer in pesticide related occupations, to calculate the meta-rate ratio, and to compare it to data from meta-analyses previously published. METHODS: A meta-analysis of 22 epidemiological studies, published between 1995 and 2001, was conducted in order to pool their rate ratio estimates. Studies were summarised and evaluated for homogeneity and publication bias. RESULTS: The meta-rate ratio estimate, based on 25 estimators of relative risk from 22 studies, was 1.13 (95% CI 1.04 to 1.22). Significant heterogeneity of rate ratios existed among the different studies. Therefore, a stratified analysis was carried out. Major sources of heterogeneity identified were geographic location, study design, and healthy worker effect. Overall, pooled risk estimates for studies derived from Europe were lower than those derived from the USA/Canada. A significant increase in rate ratio was observed for the occupation category of pesticide applicators, whereas no significant increase was observed for farmers. There was no evidence of publication bias. CONCLUSION: This increased meta-rate ratio for prostate cancer in different pesticide related occupations, including farmers, is very similar to three, previously published, meta-rate ratios for prostate cancer in farmers calculated from studies published before 1995. Although the underlying data do not identify pesticide exposure as an independent cause for prostate cancer, the fact that an increased meta-rate ratio is again obtained points to occupational exposure to pesticides as a possible factor. Future epidemiological studies should focus, as far as possible, on reliable methods to estimate actual exposure.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".