O-064 COMMON CANCERS EXCESSES AMONG EMERGENCY SERVICE WORKERS
Bibliographic record
Abstract
Abstract Introduction Much attention has been focused on identifying cancer risks among firefighters and little on other emergency service workers. We investigated cancer risks among paramedics in Ontario, Canada, and compared their results to patterns observed among firefighters and police. Methods This study used the Occupational Disease Surveillance System; 2.37 million former worker’s compensation claimants linked to the Ontario Cancer Registry. Cox proportional hazard models were used to calculate sex and age-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Results for firefighters and police were previously published, this is the first presentation of paramedic results. Results We identified 7,355 paramedics (Ncancers=289); and previously identified 13,642 firefighters (Ncancers=1,730), and 22,595 police (Ncancers=2,377). Compared to workers in all other occupational groups in the cohort, various excesses unique to these three groups were observed, but there were some striking similarities. For all three, we observed similar excesses of malignant melanoma (HRPara=2.03, CI=1.37-3.01; HRFF=2.38, CI=1.99-2.84; HRPol=2.27, CI=1.96-2.62) and prostate cancer (HRPara=1.44, CI=1.13-1.83; HRFF=1.43, CI=1.31-1.57; HRPol=1.47, CI=1.35-1.59) while decreased risks of lung cancer were observed in all three groups. Discussion Similarities between firefighters and police have been previously observed, but this is the first study to investigate cancer risks in paramedics. The assumption has generally been that cancer excesses in firefighters were due to their unique exposure. However, emergency service workers share some common carcinogenic exposures, including high-stress, vehicle exhaust, intermittent solar radiation, and night shift work. Conclusion Exploring similarities/differences between paramedics and other emergency responders may improve understanding of cancer etiology and inform primary prevention and screening efforts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".