Erectile Dysfunction in Multiple Sclerosis: A Prevalence Meta-Analysis and Systematic Review
Bibliographic record
Abstract
BACKGROUND: A connection between multiple sclerosis (MS) and erectile dysfunction (ED) has been debatable. AIM: To assess the pooled prevalence of ED among men with MS and whether MS was a risk factor for ED. METHODS: A systematic review of the literature was conducted in PubMed, Scopus, Web of Science, and the Cochrane Library to find relevant English-language studies published up to February 2022 that assessed the prevalence of ED in MS patients. Two authors independently evaluated the full text of the enrolled studies to determine eligibility, and if there was disagreement, the decision was made by a third author after discussion. Assessment tools adapted for prevalence studies were used to evaluate the quality of cross-sectional studies, and the quality of case-control studies was assessed by Newcastle-Ottawa scale. The relative risk (RR) and its 95% confidence interval (CI) were used to assess the strength of association between MS and the risk of ED. The sources of heterogeneity were investigated by subgroup analysis. Sensitivity analysis was conducted to evaluate the stability of the results. OUTCOMES: The pooled prevalence of ED in MS patients as well as 95% CIs were estimated, and the RR and its 95% CI were used to assess the strength of association between MS and the risk of ED. RESULTS: = 0.0%, P = .716). The pooled prevalence estimates of ED were 55, 63, and 57% in the age >40, IIEF diagnostic tool, and mean disease duration >10 years subgroups, respectively. CLINICAL IMPLICATIONS: The present meta-analysis indicates that MS patients had a significantly increased risk of ED, which should raise awareness of the potential association between MS and ED by clinicians. STRENGTHS & LIMITATIONS: This is the first meta-analysis to provide the global prevalence of ED in MS patients and to demonstrate that MS is a risk factor for ED. However, all enrolled studies were observational in design, which may reduce the robustness of this evidence. CONCLUSION: Results of this meta-analysis showed that ED was highly prevalent in adult men with MS and MS was a potential risk factor for ED development. Wu X, Zhang Y, Zhang W, et al. Erectile Dysfunction in Multiple Sclerosis: A Prevalence Meta-Analysis and Systematic Review. J Sex Med 2022;19:1255-1268.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".