Association between pesticide exposure and risk of kidney cancer: a meta-analysis
Bibliographic record
Abstract
This meta-analysis aimed to evaluate the correlation between pesticide exposure and kidney cancer. We conducted a systematic search of the Cochrane Library, Embase, Web of Knowledge, and Medline (updated to March 1, 2015) to identify all relevant studies. References of the retrieved articles were also identified. Fixed- or random-effect models were used to summarize the estimates of relative risk (RR) with 95% confidence interval for the association between exposure of pesticide and risk of kidney cancer. The pooled RR estimate indicated that pesticide exposure might have an elevated risk for kidney cancer (RR =1.10, 95% confidence interval 1.01-1.19). In a subgroup analysis of high quality articles, we detected that pesticide exposure is a significant risk factor for kidney cancer in a subgroup analysis of case-control studies, (Newcastle-Ottawa Quality Assessment Scale score >6) (RR =1.31, 95% confidence interval 1.12-1.51). North America studies, odds ratio studies, and studies with effect estimate adjusted for more than two confounder studies. In conclusion, pesticide exposure may be a risk factor for kidney cancer.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.066 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".