Use of a Canadian Population-Based Surveillance Cohort to Test Relationships Between Shift Work and Breast, Ovarian, and Prostate Cancer
Bibliographic record
Abstract
OBJECTIVES: Shift work with circadian disruption is a suspected human carcinogen. Additional population-representative human studies are needed and large population-based linkage cohorts have been explored as an option for surveillance shift work and cancer risk. This study uses a surveillance linkage cohort and job-exposure matrix to test relationships. METHODS: We estimated associations between shift work and breast, ovarian, and prostate cancer using the population-based Canadian Census Health and Environment Cohort (CanCHEC), linking the 1991 Canadian census to national cancer registry and mortality databases. Prevalence estimates from population labour survey data were used to estimate and assign probability of night, rotating, or evening shifts by occupation and industry. Cohort members were assigned to high (>50%), medium (>25 to 50%), low (>5 to 25%), or no (<5%) probability of exposure categories. Cox proportional hazards modelling was used to estimate associations between shift work exposure and incidence of prostate cancer in men and ovarian and breast cancer in women. RESULTS: The cohort included 1 098 935 men and 939 520 women. Hazard ratios (HRs) indicated null or inverse relationships comparing high probability to no exposure for prostate cancer: HR = 0.96, 95% confidence interval (CI) = 0.91-1.02; breast cancer: HR = 0.94, 95% CI = 0.90-0.99; and ovarian cancer: HR = 0.99, 95% CI = 0.87-1.13. CONCLUSIONS: This study showed inverse and null associations between shift work exposure and incidence of prostate, breast, or ovarian cancer. However, we explore limitations of a surveillance cohort, including a possible healthy worker survivor effect and the possibility that this relationship may require the nuanced exposure detail in primary collection studies to be measurable.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".