Shift work and colorectal cancer risk in the MCC-Spain case–control study
Bibliographic record
Abstract
Objectives Shift work that involves circadian disruption has been associated with a higher cancer risk. Most epidemiological studies to date have focused on breast cancer risk and evidence for other common tumors is limited. We evaluated the risk for colorectal cancer (CRC) in relation to shift work history in a population-based case–control study in Spain. Methods This analysis included 1626 incident CRC cases and 3378 randomly selected population controls of both sexes, enrolled in 11 regions of Spain. Sociodemographic and lifestyle information was assessed in face-to-face interviews. Shift work was assessed in detail throughout lifetime occupational history. We estimated the risk of colon and rectal cancer associated with rotating and permanent shift work (ever, cumulative duration, age of first exposure) using unconditional logistic regression analysis adjusting for potential confounders. Results Having ever performed rotating shift work (morning, evening and/or night) was associated with an increased risk for CRC [odds ratio (OR) 1.22, 95% confidence interval (95% CI) 1.04–1.43], as compared to day workers. Having ever worked permanent night shifts (≥3 nights/month) was not associated with CRC risk (OR 0.79, 95% CI 0.62–1.00). OR increased with increasing lifetime cumulative duration of rotating shift work (P-value for trend 0.005) and were highest among subjects in the top quartiles of exposure (3 rd quartile, 20–34 years, OR 1.38, 95%CI 1.06–1.81; 4 th quartile, ≥35 years, OR 1.36, 95% CI 1.02–1.79). Conclusions These data suggest that rotating shift work may increase the risk of CRC especially after long-term exposures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".