Cancer risk among firefighters and police in the Ontario workforce
Bibliographic record
Abstract
OBJECTIVE: Firefighters and police often work in high-stress, complex environments with known and suspected carcinogenic exposures. We aimed to characterise cancer incidence among firefighters and police. METHODS: The Occupational Disease Surveillance System (ODSS) was used to identify workers employed as firefighters or police in Ontario. A cohort of workers were identified using lost-time workers' compensation claims data and followed for cancer in the Ontario Cancer Registry (1983-2020). Cox proportional hazard models were used to estimate HRs and 95% CIs for primary site-specific cancer diagnoses adjusted for age at start of follow-up, birth year and sex. RESULTS: A total of 13 642 firefighters and 22 595 police were identified in the cohort. Compared with all other workers in the ODSS, firefighters and police had increased risk of prostate cancer (firefighters: HR=1.43, 95% CI 1.31 to 1.57; police: HR=1.47, 95% CI 1.35 to 1.59), colon cancer (firefighters: HR=1.39, 95% CI 1.19 to 1.63; police: HR=1.39, 95% CI 1.21 to 1.60) and skin melanoma (firefighters: HR=2.38, 95% CI 1.99 to 2.84; police: HR=2.27, 95% CI 1.96 to 2.62). Firefighters also had increased risk of cancer of the pancreas, testis and kidney, as well as non-Hodgkin's lymphoma and leukaemia. Police had increased risk of thyroid, bladder and female breast cancer. When compared directly with the police, firefighters had an elevated risk of mesothelioma and testicular cancer. CONCLUSIONS: Firefighters and police demonstrated some similar as well as some unique cancer risks. Findings from this larger worker population may have important implications for workplace and policy-level changes to improve preventative measures and reduce potential exposures to known carcinogenic hazards.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".