Significant Increase of Sexual Dysfunction in Patients with Renal Failure Receiving Renal Replacement Therapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: It has been shown that sexual dysfunction (SD) is highly prevalent among patients with chronic renal failure (CRF), and starting renal replacement therapy may even increase it. However, SD is an infrequently reported problem in these treated patients. AIM: To investigate the prevalence of SD among patients with CRF undergoing renal replacement therapy, by a meta-analysis method. METHODS: PubMed, Embase, and the Cochrane Library were systematically searched for all studies assessing sexual function in patients with CRF receiving renal replacement therapy from January 2000 to April 2020. Relative risk (RR) with 95% CIs was used for analysis to assess the risk of SD in patients with CRF receiving renal replacement therapy. The cross-sectional study quality methodology checklist was used for the cross-sectional study. The methodologic quality of the case-control and cohort studies was assessed with the Newcastle-Ottawa Scale. Data were pooled for the random-effect model. Sensitivity analyses were conducted to assess potential bias. The Begg and Egger tests were used for publication bias analysis. OUTCOMES: The prevalence of SD among patients with CRF receiving renal replacement therapy was summarized using pooled RR and 95% CI. RESULTS: = 86.1%, P = .000). Estimates of the total effects were generally consistent in the sensitivity analysis. No evidence of publication bias was observed. CLINICAL IMPLICATIONS: Patients with CRF receiving renal replacement therapy had a significantly increased risk of SD, which suggests that clinicians should evaluate sexual function, when managing patients with CRF receiving renal replacement therapy. STRENGTHS AND LIMITATIONS: This is the first study to explore the prevalence of SD among patients with CRF undergoing renal replacement therapy based on all available epidemiologic studies. However, all included studies were an observational design, which may downgrade this evidence. CONCLUSION: The prevalence of SD is significantly increased among patients with CRF receiving renal replacement therapy. More research studies are warranted to clarify the relationship. Luo L, Xiao C, Xiang Q, et al. Significant Increase of Sexual Dysfunction in Patients With Renal Failure Receiving Renal Replacement Therapy: A Systematic Review and Meta-Analysis. J Sex Med 2020;17:2382-2393.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".