Associations between Erectile Dysfunction and Vascular Parameters: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Erectile dysfunction (ED) is associated with several vascular disorders, but the associations between ED and vascular parameters are still unclear. MATERIALS AND METHODS: We analyzed and synthesized a comprehensive range of studies from PubMed, Web of Science, and Scopus regarding the associations between ED and the following measures: ankle-brachial index (ABI), pulse wave velocity (PWV), intima-media thickness (IMT), nitrate-mediated dilation (NMD), flow-mediated dilation (FMD), augmentation index (AI), endothelial progenitor cells (EPCs) and other vascular parameters. Subgroup analysis was conducted according to specific types of parameters. Study quality was assessed by using the Newcastle-Ottawa Scale. Sensitivity analysis was conducted to confirm the robustness of the pooled results. RESULTS: Fifty-seven studies with 7,312 individuals were included. Twenty-eight studies were considered to be high-quality. ED patients had a 0.11 mm higher IMT (95% confidence interval [CI]: 0.07, 0.15), a 2.86% lower FMD (95% CI: -3.56, -2.17), a 2.34% lower NMD (95% CI: -3.37, -1.31), a 2.83% higher AI (95% CI: 0.02, 5.63), a 1.11 m/s higher PWV (95% CI: 0.01, 2.21), and a 0.72% lower percentage of EPCs (95% CI: -1.19, -0.24) compared to those without ED. However, ABI was similar between ED patients and non-ED individuals. According to sensitivity analysis, the pooled results were robust. CONCLUSIONS: Our study confirmed the associations between ED and several vascular parameters and highlighted the importance of prevention and management of vascular and endothelial dysfunction in ED patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".