Association Between SARS‐COV‐2 Infection and Sperm DNA Fragmentation: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: SARS-CoV-2 infection affects various sperm quality parameters. This study examines the impact of COVID-19 infection on sperm DNA fragmentation (SDF). METHODS: A systematic literature search was performed across four databases for studies published between January 1, 2019, and January 1, 2025. The inclusion criteria focused on studies evaluating sperm DNA fragmentation in healthy men infected with the virus. The risk of bias was assessed using the Newcastle-Ottawa scale (NOS). A meta-analysis was conducted using a random effects model based on the tests employed in the studies to measure SDF. Data were reported as weighted mean differences (WMD) and corresponding 95% confidence intervals (CI). Out of 105 identified citations, seven articles were included in this analysis. The NOS results indicated that all studies were of high quality. Subgroup analysis revealed that all testing methods, including TUNEL, flow cytometry, and the sperm chromatin dispersion (SCD) test, demonstrated high heterogeneity, with the lowest heterogeneity found in the TUNEL test. RESULTS: = 99%, Z = 3.05, p < 0.0001). This meta-analysis suggests a statistically significant reduction in sperm DNA integrity 2-3 months following COVID-19 infection. CONCLUSION: However, caution is warranted when interpreting these results due to the high heterogeneity, which may affect the outcomes. A thorough analysis considering participant characteristics and infection status is recommended.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".