Association between shiftwork and the risk of colorectal cancer in females: a population-based case–control study
Bibliographic record
Abstract
OBJECTIVE: Research indicates that shiftwork may be associated with increased risks of adverse health outcomes, including some cancers. However, the evidence of an association between shiftwork and colorectal cancer risk is limited and inconclusive. Further, while several possible pathways through which shiftwork might result in cancer have been proposed, few studies have taken these factors into account. We investigated the association between two types of shiftwork (graveyard shiftwork and early-morning shiftwork) and six mechanistic shiftwork variables (including light at night and phase shift) and the risk of colorectal cancer among females in an Australian population-based case-control study. Graveyard shiftwork was the primary exposure of interest. METHODS: Participants (350 cases and 410 controls) completed a lifetime occupational history, and exposure to each of the eight shiftwork variables was assigned to participants through a job exposure matrix. We used logistic regression to calculate odds ratios (OR) and corresponding 95% confidence intervals (CI) for the association between different shiftwork variables and the risk of colorectal cancer, adjusting for potential demographic, lifestyle and medical confounders. RESULTS: Working in an occupation involving long-term exposure (>7.5 years) to graveyard shiftwork was not associated with colorectal cancer risk (adjusted OR 0.95, 95% CI 0.57 to 1.58). Similarly, no increased risks of colorectal cancer were seen for any of the other seven shiftwork variables examined. CONCLUSIONS: No evidence of an increased risk of colorectal cancer among females who had worked in occupations involving shiftwork was observed in this study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".