Farming and Prostate Cancer Mortality
Bibliographic record
Abstract
Although farmers appear to be at an increased risk of prostate cancer, trie specific exposures which produce the excess risk remain unexplained. This study was based on a retrospectively assembled cohort of male Manitoba, Saskatchewan, and Alberta, Canada, farmers age 45 years or older identified in the 1971 Canadian censuses of population and agriculture. The cohort was linked to the Canadian National Mortality Database using an iterative computer record linkage system for the period June 1971 to the end of 1987. A total of 1,148 prostate cancer deaths and 2,213,478 person-years were observed. Using Poisson regression, the study examined the relation between the risk of dying from prostate cancer and various farm practices as identified on the 1971 Census of Agriculture, including exposure to chickens, cattle, pesticides, and fuels. A weak, but statistically significant, association was found between number of acres sprayed with herbicides in 1970 and risk of prostate cancer mortality. When the analysis was restricted to farmers believed to be subject to the least amount of misclassifteation, the risk associated with acres sprayed with herbicides increased (rate ratio (RR) = 2.23 for 250 or more acres sprayed; 95 % confidence interval (Cl) 1.30-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".