Bibliographic record
Abstract
Using Ontario case-control data from the Canadian National Enhanced Cancer Surveillance system, the relationship between prostate cancer and occupational exposures was explored. Job histories obtained between 1996 and 1997 were coded for 520 pathologically confirmed prostate cancer cases and 636 population-based controls, using the Canadian Standard Occupational Classification (SOC) and the Canadian Standard Industrial (SIC) Classification. Pathology reports were reviewed for identification of men with early stage prostatic carcinoma. Four workplace substances, five industries and seven occupations were chosen for multivariate unconditional logistic regression analyses, controlling for age, marital status, body mass index, family history, education and smoking. Elevated, non-significant risk was found for exposure to PAN substances (OR = 1.22), welding (OR = 1.27), wood dust (OR = 1.24), and vinyl chloride (OR = 2.13). Men employed in the food industry had significantly elevated risk of prostate cancer (OR = 3.08), as did men in protective services (OR = 2.29) and the construction trades (OR = 1.78). Further exploration determined that carpenters had a significantly elevated risk (OR = 2.26). Removal of men with early stage cancers from the analyses did not alter the findings. A methodological sub-study, designed to ascertain reproducibility of reported occupational exposure and to compare polynuclear aromatic hydrocarbon (PAN) exposures reported in a simple versus a complex set of questions, was conducted. In 1999, 543 sub-study questionnaires were mailed; 166 cases and 164 controls responded for an overall response rate of 75%. Moderate reliability over time was found for the following exposures: asbestos (? = 0.57); PAH substances (? = 0.48); lubricating oils (? = 0.62); and, welding (? = 0.64). Wood dust showed low reliability (? = 0.19). Comparing the original responses to the new questionnaire, sensitivity and specificity were 74% and 81%, respectively, for PAH substances, and 59% and 86% for lubricating oils. The unexpected findings of increased risk for prostatic carcinoma among carpenters requires further exploration. Strategies to improve the quality of exposure classification are necessary to understand the etiologic connections between prostate cancer and occupation.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".