Bibliographic record
Abstract
Background/Rationale: Working in non-standard shifts (i.e. nights, evenings) can disrupt circadian function, and this disruption can influence hormone levels that may affect ovarian cancer risk. Few studies have examined the relationship between shift work and ovarian cancer risk, thus, more research is required in this area. The association between shift work exposure and epithelial ovarian cancer (EOC) risk, as well as risk according to tumour behaviour (borderline, invasive) and invasive cancer type (Type I, Type II) was investigated. Methods: In a population-based case-control study conducted in Montreal, Canada (2011-2016), lifetime occupational histories for 496 incident EOC cases and 906 controls were collected in a face-to-face interview. Controls were identified from the Quebec Electoral List and frequency-matched to cases on five-year age groups and electoral district. Shift work exposure for each participant was assessed using three metrics: cumulative exposure, night shift frequency, and night shift intensity. Multivariable unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between shift work exposure metric categories and overall EOC risk. Multivariable polytomous logistic regression was used to estimate associations by EOC subgroup. Associations were also examined for potential effect modification by chronotype and menopausal status. Results: Over half of cases (53.4%) and controls (51.7%) ever worked shifts that included evenings and/or nights during their lifetime. There was no strong evidence of an association between shift work exposure metrics and overall EOC risk, or by EOC subgroups, where the OR for the highest cumulative exposure tertile vs. never working shifts was 1.20 (95% CI: 0.89-1.63) for overall EOC risk. Tests for multiplicative interaction were not significant for chronotype or menopausal status, however, associations were suggested to vary among chronotypes. Additional analyses regarding cumulative exposure to specific shift timings or schedules indicated no strong association with EOC risk. Conclusions: Study findings do not provide support for the relationship between shift work exposure and EOC risk. As only three epidemiological studies have been published looking at this association, further investigation is warranted.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".