Host Factors, Occupation, and Testicular Cancer in Saskatchewan, Canada: 1979-2002
Bibliographic record
Abstract
The incidence rates of testicular cancer are increasing in several countries, especially among younger adults. The role of agricultural exposure in the etiology of testicular cancer is contentious. We extracted information related to the host, lifestyle, and tumor characteristics from the files of the Saskatchewan Cancer Agency for all cases (n = 517) of testicular cancer diagnosed in Saskatchewan between 1979 and 2000. The following questions were the subject of this initial inquiry: (1) Are tumor characteristics similar or different among occupational groups dichotomized into farmer/nonfarmer? (2) Are host characteristics similar or different among occupational groups? (3) Is farming as an occupation one of the independent predictors of tumor stage at diagnosis? Statistical analyses were restricted on 486 cases. The nonfarmers (n = 349) had smaller tumors in length on average, and more of them were diagnosed at stage I compared to farmers (n = 72). Occupation was not recorded for 65 cases. Farmers were older than nonfarmers. In logistic regression analyses with adjustment for relevant variables as cited in the literature, individuals with nonseminomas [OR (95% CI) 1.99 (1.30-3.31)] and < or = 26 years old at diagnosis [2.12 (1.15-3.93)] were significantly more likely to be diagnosed with a stage 2 or higher tumor. Farmers were significantly more likely than nonfarmers to be diagnosed at stage 2 or higher [1.76 (1.00-3.10)]. Based on our data, the significant predictors of being diagnosed with stage 2 and higher are: presence of nonseminoma, < or = 26 years old, and farming as an 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".