Chemical exposures other than arsenic are probably not important risk factors for squamous cell carcinoma, basal cell carcinoma and malignant melanoma of the skin
Bibliographic record
Abstract
Conflicts of interest: none decleared. Sir, Chemical substances, including pesticides, industrial substances such as polycyclic aromatic hydrocarbons (PAHs) and arsenic compounds, may increase cancer risk through genotoxicity, tumour promotion, hormonal action and immunotoxicity.1 Pesticides can be classified as insecticides, herbicides and fungicides.2 Significant exposures are common in farmers. A definite association between pesticides and skin cancer has not been clearly established. Some studies utilizing case–control or cohort study designs have reported associations,3, 4 but other studies have failed to confirm those results.5, 6 An association between PAHs and cancer has been reported by several authors.7, 8 People can be exposed to PAHs if they work in coal, iron and steel foundries, or industries associated with coal gasification, tar distillation, shale oil extraction, roofing, road paving or wood impregnation. Using data from a large case–control study, consisting of 161 patients with squamous cell carcinoma (SCC), 302 patients with nodular basal cell carcinoma (BCC), 152 patients with superficial multifocal BCC, 125 patients with malignant melanoma and 386 controls,8 we evaluated exposures to pesticides, PAHs, arsenic and asbestos, and their relation to the risk of these skin cancers. Information about exposures to chemical compounds and other factors was collected in personal interviews. Pesticide exposure was calculated by multiplying the number of days of exposure per year with the years of exposure during lifetime. Among the 164 individuals who were exposed, the median number of lifetime days of exposure was 126. Exposure below the median was defined as ‘low’; exposure above the median was defined as ‘high’. Exposures to all the other chemical substances were classified as ‘never’ or ‘ever’. Odds ratios (ORs) were calculated for the different chemical exposures and interpreted as relative risks for developing skin cancer. Logistic regression was used to calculate the ORs while adjusting for age, skin type and smoking. In all, there were 466 men and 500 women. Because exposures were relatively rare among women, we estimated risks for men only.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.016 |
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".