Bibliographic record
Abstract
This study assesses the effect of occupational exposure to specific chemicals on the risk of renal cell carcinoma in Canada. Mailed questionnaires were used to obtain data on 1279 (691 male and 588 female) newly diagnosed, histologically confirmed renal cell carcinoma cases and 5370 population controls in eight Canadian provinces, between 1994 and 1997. Data were collected on socio-economic status, smoking habit, alcohol use, diet, residential and occupational histories, and years of exposure to any of 17 chemicals. Odds ratios (ORs) and 95% confidence intervals (CIs) were derived using unconditional logistic regression. The study found an increased risk of renal cell carcinoma in males only, which was associated with occupational exposure to benzene; benzidine; coal tar, soot, pitch, creosote or asphalt; herbicides; mineral, cutting or lubricating oil; mustard gas; pesticides; and vinyl chloride. Compared with no exposure to the specific chemical, the adjusted ORs were 1.8 (95% CI = 1.2-2.6), 2.1 (1.3-3.6), 1.4 (1.1-1.8), 1.6 (1.3-2.0), 1.3 (1.1-1.7), 4.6 (1.7-12.5), 1.8 (1.4-2.3) and 2.0 (1.2-3.3), respectively; an elevated risk was also associated with exposure to cadmium salts and isopropyl oil. The risk of renal cell carcinoma increased with duration of exposure to benzene, benzidine, cadmium, herbicides and vinyl chloride. Very few females were exposed to specific chemicals in this study; further research is needed to clarify the association between occupational exposure to chemicals and renal cell carcinoma in females.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".