Sleep duration and napping in relation to colorectal and gastric cancer in the MCC-Spain study
Bibliographic record
Abstract
Abstract Sleep duration is a novel and potentially modifiable risk factor for cancer. We evaluated the association of self-reported sleep duration and daytime napping with odds of colorectal and gastric cancer. We included 2008 incident colorectal cancer cases, 542 gastric cancer cases and 3622 frequency-matched population controls, recruited in the MCC-Spain case–control study (2008–2013). Sleep information, socio-demographic and lifestyle characteristics were obtained through personal interviews. Multivariable adjusted logistic regression models were used to estimate odds ratios (OR) with 95% confidence intervals (CI) for cancer, across categories of sleep duration (≤ 5, 6, 7, 8, ≥ 9 hours/day), daytime napping frequency (naps/week) and duration (minutes/nap). Compared to 7 hours of sleep, long sleep was associated with increased odds of colorectal (OR ≥9 hours : 1.59; 95%CI 1.30–1.94) and gastric cancer (OR ≥9 hours : 1.95; 1.37–2.76); short sleep was associated with increased odds of gastric cancer (OR ≤5 hours : 1.32; 0.93–1.88). Frequent and long daytime naps increased the odds of colorectal (OR 6–7 naps/week, ≥30 min : 1.32; 1.14–1.54) and gastric cancer (OR 6–7 naps/week, ≥30 min : 1.56; 1.21–2.02). Effects of short sleep and frequent long naps were stronger among participants with night shift-work history. Sleep and circadian disruption may jointly play a role in the etiology of colorectal and gastric cancer.
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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 | 0.000 | 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".