Association Between HIV Infection and Prevalence of Erectile Dysfunction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of erectile dysfunction (ED) in men positive for HIV has been reported to exceed the baseline of the general population. However, no meta-analysis or conclusive review has investigated whether individuals with HIV infection have a significantly higher prevalence of ED. AIM: To explore the exact association between HIV infection and the prevalence of ED. METHODS: The PubMed, Embase, Medline, and Cochrane Library databases were searched to identify studies concerning the association between HIV infection and the prevalence of ED that were published up to December 2016. Manual searches also were performed. Relative risks and corresponding 95% confidence intervals were used to estimate the strength of association between HIV infection and the prevalence of ED. The methodologic quality of the included cohort studies was assessed through the Newcastle-Ottawa Scale. The cross-sectional study quality methodology checklist was used to assess the quality of cross-sectional studies. Sensitivity analyses were conducted to assess potential bias. This study was conducted according to the guidelines for Meta-Analyses and Systematic Reviews of Observational Studies (MOOSE). OUTCOMES: The strength of association between HIV infection and the prevalence of ED was evaluated using summarized unadjusted pooled relative risks and 95% confidence intervals. RESULTS: = 84%, P < .001). Estimates of total effects were generally consistent with the sensitivity. CLINICAL IMPLICATIONS: Individuals with HIV infection had a significantly increased prevalence of ED, which suggests that ED should be of concern to clinicians when managing men with HIV infection. STRENGTHS AND LIMITATIONS: A strength of this study is that it is the first meta-analysis to explore the relation between HIV infection and the prevalence of ED. A limitation is that all included studies were observational studies, which can induce recall bias or selection bias. CONCLUSION: Evidence from the observational studies suggested that individuals with HIV infection had a significantly increased prevalence of ED despite significant heterogeneity. More research is warranted to clarify the relation between HIV infection and the prevalence of ED. Luo L, Deng T, Zhao S, et al. Association Between HIV Infection and Prevalence of Erectile Dysfunction: A Systematic Review and Meta-Analysis. J Sex Med 2017;14:1125-1132.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".