Cancer incidence in male and female Canadian Armed Forces personnel and Veterans enrolled between 1976 and 2016: a retrospective population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To describe rates of overall and type-specific primary cancers in Canadian Armed Forces (CAF) personnel and Veterans with a first enrolment in the CAF between 1976 and 2016, with comparisons to the Canadian general population (CGP). METHODS: This retrospective cohort study linked CAF administrative data to national cancer registries. Primary cancer diagnoses were ascertained from 1976 to 2017. Using age, year and sex-specific rates from the CGP, SIRs and 95% CIs were calculated by sex for all cancers combined and specific cancer types. Subgroup analyses were conducted for service status, rank and international deployment. RESULTS: Among 210 910 male and 34 940 female CAF personnel and Veterans, 6415 and 1620 incident primary cancer cases were observed, respectively. For cancers overall, CAF personnel and Veterans had lower or similar risk compared with the CGP. Subgroup analyses indicated lower or similar risk compared with the CGP for most cancer types but elevated risk for melanoma in male and female personnel, officers, deployers and male senior non-commissioned members (NCMs); lung and bronchus cancer in male and female junior NCMs; pancreatic cancer in male junior NCMs; testicular cancer in male officers; and cervical cancer in female junior NCMs and non-deployers. CONCLUSION: CAF personnel and Veterans had lower or equal rates of cancer overall compared with the CGP. Elevated rates were observed for certain cancers within subgroups. Further research to examine time trends and risk factors for cancer outcomes in this population is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".