Occupational exposure to solar ultraviolet radiation and the risk of prostate cancer
Bibliographic record
Abstract
OBJECTIVES: Preventable risk factors for prostate cancer are poorly understood; sun exposure is a possible protective factor. The goal of this study was to investigate prostate cancer risk in outdoor workers, a population with high sun exposure. METHODS: Prostate cancer cases and controls from a large study (conducted between 1994 and 1997) were used for this analysis. A job exposure matrix (JEM) was used to assign solar ultraviolet radiation (UVR) at work as moderate (2 to <6 hours outside/day) or high (≥6 hours). Average daily satellite UV-B measures were linked to the latitude/longitude of the residences of each participant. Several other exposure metrics were also examined, including ever/never exposed and standard erythemal dose by years (SED×years). Logistic regression was used to evaluate the association between solar UVR exposure and the odds of prostate cancer. RESULTS: A total of 1638 cases and 1697 controls were included. Men of Indian and Asian descent had reduced odds of prostate cancer (ORs 0.17 (0.08 to 0.35) and 0.25 (0.15 to 0.41), respectively) compared with Caucasian men, as did single men (OR 0.76 (0.58 to 0.98)) compared with married men. Overall, no statistically significant associations were observed between sun exposure and prostate cancer with 1 exception. In the satellite-enhanced JEM that considered exposure in high category jobs only, prostate cancer odds in the highest quartile of cumulative exposure was decreased compared with unexposed men (OR 0.68 (0.51 to 0.92)). CONCLUSIONS: This study found limited evidence for an association with prostate cancer, with the exception of 1 statistically significant finding of a decreased risk among workers with the longest term and highest sun exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".