Shift Work Patterns, Chronotype, and Epithelial Ovarian Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Shift work causing circadian disruption is classified as a "probable carcinogen" and may contribute to the pathogenesis of hormone-sensitive cancers. This study investigated shift work exposure in relation to epithelial ovarian cancer (EOC) risk. METHODS: In a population-based case-control study with 496 EOC cases and 906 controls, lifetime occupational histories were collected and used to calculate cumulative years of shift work exposure, average number of night shifts per month, and average number of consecutive night shifts per month. ORs and 95% confidence intervals (CI) for associations with EOC risk were estimated using logistic regression. Associations were also examined according to chronotype and menopausal status. RESULTS: More than half of the cases (53.4%) and controls (51.7%) worked evening and/or night shifts. There was no clear pattern of increasing EOC risk with increasing years of shift work; the adjusted OR of EOC comparing the highest shift work category versus never working shift work was 1.20 (95% CI, 0.89-1.63). This association was more pronounced among those self-identified as having a "morning" chronotype (OR, 1.64; 95% CI, 1.01-2.65). Associations did not greatly differ by menopausal status. CONCLUSIONS: These results do not strongly demonstrate a relationship between shift work and EOC risk. IMPACT: This study collected detailed shift work information and examined shift work patterns according to shift times and schedules. The findings highlight that chronotype should be considered in studies of shift work as an exposure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".