Sleep and cancer incidence in Alberta’s Tomorrow Project cohort
Bibliographic record
Abstract
STUDY OBJECTIVES: Few studies have examined associations between sleep duration with combined and site-specific cancers within the same cohort. Additionally, no study to date has assessed associations between sleep timing midpoint and cancer incidence. Therefore, we aimed to investigate associations between self-reported sleep duration and sleep timing midpoint with combined and site-specific cancer incidence in Alberta's Tomorrow Project (ATP) cohort. METHODS: The sleep duration analysis included 45,984 Albertans aged 35-69 years recruited from 2001-2015. Sleep timing midpoint (wake-time - ½ sleep duration) was assessed in a subset of ATP participants (n = 19,822). Incident cancer cases were determined through linkage with the Alberta Cancer Registry in June 2017. Cox proportional hazard regression models evaluated the effects of sleep duration and sleep timing midpoint on combined and seven site-specific cancers. RESULTS: A total of 2,428 and 1,322 incident cancer cases were observed in the sleep duration and sleep timing analyses, respectively. Reporting >9 h of sleep/night versus 7-9 h of sleep/night was associated with an increased incidence of non-Hodgkin lymphoma (hazard ratio [HR] = 2.14, 95% confidence interval [CI]: 1.14-4.01; p = 0.02) and hematological (HR = 1.70, 95% CI: 1.03-2.82; p = 0.04) cancers. A later sleep timing midpoint (>4 h 8 min) versus an intermediate sleep timing midpoint (3 h 47 min-4 h 8 min) was associated with an increased incidence of combined (HR = 1.20, 95% CI: 1.04-1.37; p = 0.01) and breast (HR = 1.49, 95% CI: 1.09-2.03; p = 0.01) cancers. CONCLUSIONS: Sleep duration and sleep timing may play a role in cancer etiology. Studies including objective sleep assessments are needed to corroborate these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".