Risk of Cancer Among Firefighters: A Quantitative Review of Selected Malignancies
Bibliographic record
Abstract
Using the fixed-effect model, the author quantitatively estimated the risks of cancers of the colon, bladder, kidneys, and brain as well as non-Hodgkin's lymphoma and leukemia among firefighters. The risk of these six cancers was not markedly elevated when cohort mortality studies were considered. When all mortality studies were considered, however, there was a mild increase in risk for kidney cancer and non-Hodgkin's lymphoma, with a summary relative risk (sumRR) of 1.22 (95% confidence interval [CI] = 1.02-1.43) and 1.40 (95% CI = 1.20-1.60), respectively. A subcohort analysis based on duration of employment revealed that firefighters with 30 or more years of employment had a significantly increased mortality risk for colon cancer, sumRR of 1.51 (95% CI = 1.05-2.11); kidney cancer, sumRR of 6.25 (95% CI = 1.70-16.00); brain cancer, sumRR of 2.53 (95% CI = 1.27 7.07); and leukemia, sumRR of 2.87 (95% CI = 1.43-5.14). After firefighters had 40 or more years of employment, their risk of mortality was significantly increased for colon cancer, sumRR of 4.71 (95% CI = 2.03-9.27); kidney cancer, sumRR of 36.12 (95% CI = 4.03-120.42); and bladder cancer, sumRR of 5.7 (95% CI = 1.56-14.63). The risk for non-Hodgkin's lymphoma was elevated but not significantly so among firefighters with 20 or more years of employment, with sumRR of 1.72 (95% CI = 0.90-3.31). Kidney cancer risk was significantly elevated as early as the second decade of employment.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".