Occupational exposure to pesticides and prostate cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Epidemiological studies on exposure to pesticides and risk of prostate cancer (PC) provide inconsistent results. We aimed to explore various potential sources of heterogeneity not previously assessed and to derive updated risk estimates from homogenous studies. We searched PubMed, Web of Science and Scopus databases for case-control and cohort studies published from 1985 to April 2014. We assessed the quality of the articles using the Newcastle-Ottawa Scale. Pooled estimates were calculated using random-effects models. Heterogeneity was explored using subset analyses and metaregression. Fifty-two studies were included in the review and 25 in the meta-analysis. No association was found between low exposure to pesticides and PC, but association was significant for high exposure, pooled OR 1.33 (1.02 to 1.63), I(2)=44.8%, p=0.024. Heterogeneity was explained by a number of variables including method used to assess exposure. Pooled OR was weak and non-significant for studies measuring serum pesticide level, 1.12 (0.74 to 1.50), I(2)=0.00%, p=0.966. For studies applying self-reporting of exposure, pooled estimate was 1.34 (0.91 to 1.77), I(2)=0.00%, p=0.493, while a high significant association was detected for grouped exposure assessment, 2.24 (1.36 to 3.11), I(2)=0.00%, p=0.955. In spite of a weak significant association detected when pooling ORs for high occupational exposure to pesticides, the magnitude of the association was related to the method of exposure assessment used by the original studies. A family history-pesticide exposure interaction was also observed for a number of pesticides.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".