Mortality and cancer incidence in a nickel cohort
Bibliographic record
Abstract
BACKGROUND: Previous studies of nickel workers have primarily noted significant early increases in lung and nasal cancers and for various types of accidents. AIMS: To examine cancer incidence and mortality, concurrently, for a cohort of male nickel workers at a major nickel and copper producer in Sudbury, Ontario, Canada. METHODS: From January 1964 to December 2001, nominal roll and work history information were linked to Ontario health data and mortality and cancer incidence were compared to the Ontario population. RESULTS: There were 1984 (19%) deaths and 1127 (11%) incident cancers (n = 10,253). Significant elevations in mortality were observed for accidents, poisoning and violence; for possibly job-related accidents among those with <15 years since first hire [standardized mortality ratio (SMR) = 133, 95% CI: 111-158; SMR = 241, 95% CI: 159-351, respectively] and for accidents in those with > or =15 years since first hire (SMR = 123, 95% CI: 104-144). Significant elevations were also observed for accidents, poisoning and violence for those with 6 months to 14 years work experience and for lung cancer incidence and mortality for those with 15-29 years work experience (SMR = 128, 95% CI: 107-153). Incident lung cancers were significantly elevated for those hired in the 1940s and 1960s. CONCLUSIONS: Significant lung cancer mortality and incidence elevations were observed for the cohort and underground workers with increased time since first hire, for those hired during early periods of operation and for those with longer durations of employment. Further aetiological study is required as occupational aetiology could not be ascertained.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".