Occupational exposure to chlorinated solvents and kidney cancer: a case–control study
Bibliographic record
Abstract
OBJECTIVES: Trichloroethylene, a chlorinated solvent widely used for metal degreasing, is classified by the International Agency for Research on Cancer as a kidney carcinogen. Other chlorinated solvents are suspected carcinogens, most notably the cleaning solvent perchloroethylene, although it is unclear whether they are associated with kidney cancer. We investigated kidney cancer associations with occupational exposure to 6 chlorinated solvents (trichloroethylene, perchloroethylene, 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride) within a case-control study using detailed exposure assessment methods. METHODS: Cases (n=1217) and controls (n=1235) provided information on their occupational histories and, for selected occupations, on tasks involving potential exposure to chlorinated solvents through job-specific interview modules. Using this information, an industrial hygienist assessed potential exposure to each solvent. We computed ORs and 95% CIs for different exposure metrics, with unexposed participants as the referent group. RESULTS: 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride were not associated with kidney cancer. Among jobs with high exposure intensity, high cumulative hours exposed to perchloroethylene was associated with increased risk, both overall (third tertile vs unexposed: OR 3.1, 95% CI 1.3 to 7.4) and after excluding participants with ≥50% exposure probability for trichloroethylene (OR 3.0, 95% CI 0.99 to 9.0). A non-significant association with high cumulative hours exposed to trichloroethylene was observed (OR 1.7, 95% CI 0.8 to 3.8). CONCLUSIONS: In this study, high exposure to perchloroethylene was associated with kidney cancer, independent of trichloroethylene. Additional studies are needed to further investigate this finding.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".