Sperm DNA Fragmentation in Men with Varicocele: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: In this systematic review and meta-analysis, we investigated the relationship between varicocele and sperm DNA fragmentation (SDF) alongside conventional semen parameters. MATERIALS AND METHODS: A comprehensive systematic search was conducted to identify articles on SDF in men with varicocele published in the Scopus and PubMed databases. This included observational studies investigating levels of SDF as a primary outcome and conventional semen parameters as secondary outcomes in men with varicocele compared to men without varicocele. RESULTS: Out of 476 retrieved articles, 40 met our inclusion criteria, with 35 included in our quantitative synthesis. Our analysis revealed that men with varicocele showed significantly higher SDF than men without varicocele (mean difference [MD] 15.34, 95% confidence interval [95% CI]: 9.74-20.9; p<0.001). The evaluation of SDF using different assays did not affect the results (p=0.7). Our results indicated a significant effect of varicocele on sperm concentration (MD -29 million/mL, 95% CI: -37, -21; p<0.001), total sperm count (MD -188 million/ejaculate, 95% CI: -325, -52; p=0.007), sperm vitality (MD -5.7%, 95% CI: -8.6, -2.8; p<0.001), total sperm motility (MD -17%, 95% CI: -29.0, -5.3; p=0.004), progressive sperm motility (MD -17%, 95% CI: -23,-11, p<0.001), normal sperm forms (MD -3.1%, 95% CI: -4.3, -1.8, p<0.001) and abnormal sperm forms (MD 2.25%, 95% CI: 0.77, 3.73; p=0.003). However, semen volume and the total motile sperm count were not significantly different between both groups (MD -113 million/ejaculate, 95% CI: -244.0, 18.1; p=0.09). CONCLUSIONS: Varicocele negatively impacts male fertility by increasing SDF. Our study supports using SDF analysis in patients with varicocele to aid clinicians in deciding on varicocele repair.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 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 | 0.002 |
| 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".