Impact of obstructive sleep apnea on aortic disease occurrence: A meta-analysis
Bibliographic record
Abstract
Objective: Aortic diseases, mainly including aortic dilatation, aortic aneurysm (AA) and aortic dissection (AD), have high morbidity and mortality. Many studies have suggested that obstructive sleep apnea (OSA) acts as a candidate risk factor for aortic diseases. Thus, we performed a meta-analysis to explore comprehensively the effect of OSA on the risk of aortic disease occurrence. Methods: We searched PubMed, Embase and Cochrane Library databases from inception to February 2022 to identify studies investigating the association between OSA and aortic diameter dilatation, the prevalence of OSA in individuals with or without AA/AD and the incidence of AA/AD in individuals with or without OSA. The Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) were respectively used to evaluate the quality of the included cohort and cross-sectional studies. A random or fixed effect model was used to generate pooled effects according to interstudy heterogeneity. Sensitivity analyses were performed to test the robustness of the results. Results: We identified 10 observational publications with 214,127 participants in this meta-analysis. OSA was significantly associated with increased aortic diameter (WMD = 1.46, 95% CI, 1.10-1.83, p < 0.001). OSA prevalence was higher in patients with AA/AD compared to their counterparts without AA/AD (OR = 1.90, 95% CI, 1.30-2.76, p = 0.001). No significant difference in the incidence of AA/AD was observed in individuals with or without OSA (RR = 0.85, 95% CI, 0.62-1.16, p = 0.307). Sensitivity analyses did not modify these results. Conclusions: This meta-analysis suggests that OSA is associated with aortic diameter dilatation but does not affect AA/AD occurrence.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.067 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".