Incidence of cancer among Nordic police officers
Bibliographic record
Abstract
Police work may expose officers to various circumstances that have potential for increasing their risk of cancer, including traffic-related air pollution, night shift work and radiation from radars. In this study, we examined the incidence of cancer among Nordic male and female police officers. We utilize data from the Nordic Occupational Cancer (NOCCA) project, which linked census data on occupations from Finland, Iceland, Norway and Sweden to national cancer registries for the period 1961 to 2005. We report standardized incidence ratios (SIR) and 95% confidence intervals (CI) of selected cancers for each country by sex, age and calendar period. The cohort included 38 523 male and 1998 female police officers. As compared with the general population, male police officers had a 7% (95% CI: 4-9%) excess cancer risk, with elevated SIRs for various cancer sites, including prostate (SIR 1.19, 1.14-1.25), breast (SIR 1.77, 1.05-2.80), colon (SIR 1.22, 1.12-1.32) and skin melanoma (SIR 1.44, 1.28-1.60). Conversely, male police officers had a lower risk of lung cancer than the general population (SIR 0.72, 0.66-0.77). In female police officers, the SIR for cancer overall was 1.15 (0.98-1.34), and there was a slight excess of cancers of the breast (SIR 1.25, 0.97-1.59) and colon (SIR 1.21, 0.55-2.30). In conclusion, cancer incidence among the police officers was slightly higher than in the general population. Notably, SIRs were elevated for cancer sites potentially related to night shift work, namely colon, breast and prostate cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".