Obstructive Sleep Apnea and Lung Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Rationale In 2020, lung cancer was the leading cause of cancer deaths and the most common cancer in men. Although obstructive sleep apnea (OSA) has been postulated to be carcinogenic, epidemiological studies are inconclusive. Objectives To investigate the associations between OSA and the incidence and mortality of lung cancer. Methods Four electronic databases (PubMed, Embase, Cochrane Library, and Scopus) were searched from inception until 6 June 2021 for randomized controlled trials and observational studies examining the association between sleep apnea and incident lung cancer. Two reviewers selected studies, extracted data, graded the risk of bias using the Newcastle-Ottawa scale and the quality of evidence using the Grading of Recommendations Assessment, Development, and Evaluation system. Random-effects models were used to meta-analyze the maximally covariate-adjusted associations. Results Seven studies were included in our systematic review, among which four were suitable for meta-analysis, comprising a combined cohort of 4,885,518 patients. Risk of bias was low to moderate. OSA was associated with a higher incidence of lung cancer (hazard ratio, 1.25; 95% confidence interval, 1.02–1.53), with substantial heterogeneity (I 2 = 97%). Heterogeneity was eliminated, with a stable pooled effect size, when including the three studies with at least 5 years of median follow-up (hazard ratio, 1.32; 95% confidence interval, 1.27–1.37; I 2 = 0%). Conclusions In this meta-analysis of 4,885,518 patients from four observational studies, patients with OSA had an approximately 30% higher risk of lung cancer compared with those without OSA. We suggest more clinical studies with longer follow-up as well as biological models of lung cancer be performed to further elucidate this relationship.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".