Cancer Mortality among Males in Relation to Exposures Assessed through a Job-exposure Matrix
Bibliographic record
Abstract
To identify potential associations between workplace exposures and cancer mortality risks, job titles collected from 1965 to 1971 for 58,678 men (a subset of a large representative sample of the Canadian workforce) were transformed into probable chemical exposures using a job-exposure matrix developed in Montreal. Mortality follow-up was determined through computerized record linkage with the National Mortality Database in Canada for 1965-1991. Cancer mortality risk was evaluated at two levels of exposure, any and substantial, using Poisson regression controlling for age, calendar period, and social class. Among the 58,678 men, 3,160 died of cancer. Using a liberal reporting criterion, relative risk (RR) >1.0, five or more exposed cancer deaths, p < or = 0.100, several potential associations were identified, including: lung cancer and any exposure to abrasives dust (RR = 2.84), prostate cancer and any exposure to calcium carbonate (RR = 2.46), and prostate cancer and substantial exposure to metallic dust (RR = 2.13).
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 | 0.001 | 0.003 |
| 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".