The Canadian Forces Cancer and Mortality Study II: a longitudinal record-linkage study protocol
Bibliographic record
Abstract
BACKGROUND: Military service exposes personnel to unusual situations with unclear health-related implications, and to identify both immediate and delayed risks, part of health surveillance includes examination of mortality and cancer rates that extends beyond periods of military service. The main aim of the Canadian Forces Cancer and Mortality Study II (CFCAMS II) is to describe the mortality and cancer experience of Canadian Armed Forces personnel (serving and released; about 230 000 people), with the further aim of informing health promotion and prevention programs for serving personnel and services for veterans after they leave the military. METHODS: This protocol is for a retrospective cohort study of serving and released Canadian Armed Forces personnel who enrolled on or after Jan. 1, 1976 in the Regular Force or Class C of the Reserve Force. To create our cohort, we identified record-linkage methods as the most appropriate mechanism to study mortality and cancer in those with a history of Canadian military service. Statistics Canada will link the CFCAMS II cohort file to the Canadian Vital Statistics (Mortality) and Canadian Cancer Registry databases for outcomes up to Dec. 31, 2014. The linkage will be stored in their highly secure linkage environment. Statistical analyses will be broadly divided into mortality and cancer incidence. RESULTS: We will quantify mortality and cancer morbidity incidence and survival using multiple established methods, as well as age-period-cohort regression models to describe the relation between military service and mortality and cancer outcomes. INTERPRETATION: The findings will represent novel and sound evidence on the risks and protective factors of military life.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".