Association Between Hearing Loss and Cardiovascular Disease: A Meta‐analysis
Bibliographic record
Abstract
Abstract Objective Hearing loss (HL) has been postulated to be linked to cardiovascular diseases (CVDs) via vascular mechanisms, but epidemiological associations remain unclear. The study aims to clarify the association between HL and stroke, coronary artery disease (CAD), and any CVD. Data Sources PubMed, Embase, and SCOPUS from inception until April 27, 2022. Review Methods Three blinded reviewers selected observational studies reporting stroke, CAD, and any CVD in patients with HL, compared to individuals without HL. We extracted data, evaluated study bias using the Newcastle‐Ottawa scale, following Preferred Reporting Items for Systematic Reviews and Meta‐analyses guidelines and a PROSPERO‐registered protocol (CRD42022348648). We used random‐effects inverse variance meta‐analyses to pool the odds ratios (ORs) for the association of HL with stroke, CAD, and any CVD. Results We included 4 cohort studies (N = 940,771) and 6 cross‐sectional studies (N = 680,349). Stroke, CAD, and any CVD were all strongly associated with HL. The overall pooled OR of the association between HL and stroke was 1.26 (95% confidence interval [CI] = 1.16‐1.37, I2 = 78%), and was 1.33 (95% CI = 1.12‐1.58) and 1.29 (95% CI = 1.14‐1.45) for low‐ and high‐frequency HL, respectively. Minimal publication bias was observed, with minimal change to pooled effect size following trim and fill. Similarly, the pooled OR of the association between HL and CAD was 1.36 (95% CI = 1.13‐1.64, I2 = 96%), while that between HL and any CVD was 1.38 (95% CI = 1.07‐1.77, I2 = 99%). Conclusion Our findings suggest that HL and CVD are closely related. Physicians treating patients with HL should be cognizant of this association and view HL in the broader context of general health and aging.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.048 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".