Comparison of cancer incidence and mortality in the Norwegian Fire Departments Cohort, 1960–2018
Bibliographic record
Abstract
OBJECTIVES: Elevated risk of cancer at several sites has been reported among firefighters, although with mixed findings. The purpose of this study was to calculate standardised incidence ratios (SIRs) and standardised mortality ratios (SMRs) for cancer and compare them to assess whether use of the different measures could be a source of inconsistencies in findings. METHODS: The Norwegian Fire Departments Cohort, comprising 4295 male employees who worked at 15 fire departments across Norway, was linked to health outcome registries for the period 1960-2018. SIRs and SMRs were derived using national reference rates. RESULTS: Overall, we observed elevated incidence of colon cancer (SIR, 95% CI 1.27, 1.01 to 1.58), mesothelioma (2.59, 1.12 to 5.11), prostate cancer (1.18, 1.03 to 1.34) and all sites combined (1.15, 1.08 to 1.23). Smaller, non-significant elevations were found for mortality of colon cancer (SMR, 95% CI 1.20, 0.84 to 1.67) and mesothelioma (1.66, 0.34 to 4.86), while SMR for prostate cancer was at unity. Potential errors were observed in some of the mortality data, notably for mesothelioma cases. Among those who died of cancer, 3.7% (n=14) did not have a prior diagnosis of malignancy at the same site group. CONCLUSIONS: Assessment of incidence or mortality did not greatly influence the interpretation of results. The most prominent differences in SIR and SMR appeared to be due to inconsistencies between sites of cancer diagnosis and cause of death. The difference in SIR and SMR for prostate cancer suggested a detection bias from differential screening practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".