Obstructive sleep apnea and the prevalence and incidence of cancer
Bibliographic record
Abstract
BACKGROUND: A link between obstructive sleep apnea and cancer development or progression has been suggested, possibly through chronic hypoxemia, but supporting evidence is limited. We examined the association between the severity of obstructive sleep apnea and prevalent and incident cancer, controlling for known risk factors for cancer development. METHODS: We included all adults referred with possible obstructive sleep apnea who underwent a first diagnostic sleep study at a single large academic hospital between 1994 and 2010. We linked patient data with data from Ontario health administrative databases from 1991 to 2013. Cancer diagnosis was derived from the Ontario Cancer Registry. We assessed the cross-sectional association between obstructive sleep apnea and prevalent cancer at the time of the sleep study (baseline) using logistic regression analysis. Cox regression models were used to investigate the association between obstructive sleep apnea and incident cancer among patients free of cancer at baseline. RESULTS: Of 10 149 patients who underwent a sleep study, 520 (5.1%) had a cancer diagnosis at baseline. Over a median follow-up of 7.8 years, 627 (6.5%) of the 9629 patients who were free of cancer at baseline had incident cancer. In multivariable regression models, the severity of sleep apnea was not significantly associated with either prevalent or incident cancer after adjustment for age, sex, body mass index and smoking status at baseline (apnea-hypopnea index > 30 v. < 5: adjusted odds ratio [OR] 0.96, 95% confidence interval [CI] 0.71-1.30, for prevalent cancer, and adjusted hazard ratio [HR] 1.02, 95% CI 0.80-1.31, for incident cancer; sleep time spent with oxygen saturation < 90%, per 10-minute increase: adjusted OR 1.01, 95% CI 1.00-1.03, for prevalent cancer, and adjusted HR 1.00, 95% CI 0.99-1.02, for incident cancer). INTERPRETATION: In a large cohort, the severity of obstructive sleep apnea was not independently associated with either prevalent or incident cancer. Additional studies are needed to elucidate whether there is an independent association with specific types of 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 | 0.004 |
| 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 | 0.000 | 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".