Cancer incidence and mortality among firefighters
Bibliographic record
Abstract
Firefighters are exposed to both known and suspected carcinogens. This study aims to systematically review the literature on the association of firefighting occupation and cancer incidence and mortality, overall and for specific cancer sites. A systematic review using PubMed, Embase, and Web of Science was performed up to January 1, 2018. We extracted risk estimates of cancers and calculated summary incidence risk estimates (SIRE), summary mortality risk estimates (SMRE), and their 95% confidence intervals (CI). Publication bias and risk of bias in individual studies were assessed using Begg's and Egger's tests and the Newcastle-Ottawa scale (NOS), respectively. We included 50 papers in the review and 48 in the meta-analysis. We found significantly elevated SIREs for cancer of the colon (1.14; CI 1.06 to 1.21), rectum (1.09; CI 1.00 to 1.20), prostate (1.15; CI 1.05 to 1.27), testis (1.34; CI 1.08 to 1.68), bladder (1.12; CI 1.04 to 1.21), thyroid (1.22; CI 1.01 to 1.48), pleura (1.60; CI 1.09 to 2.34), and for malignant melanoma (1.21; CI 1.02 to 1.45). We found significant SMREs of 1.36 (1.18 to 1.57) and 1.42 (1.05 to 1.90) for rectal cancer and Non-Hodgkin's lymphoma, respectively. Considering the significantly elevated risk of some cancers in this occupational group, we suggest improving preventive measures and securing adequate and relevant medical attention for this group. Further studies with more accurate and in-depth exposure assessments are indicated.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".