A Systemic Review and Meta-analysis of the Effect of SARS-CoV-2 Infection on Sperm Parameters
Bibliographic record
Abstract
Objective . Several studies examined the putative effects of SARS-CoV-2 infection on sperm parameters. However, the results remain controversial. In this study, we conducted the most up-to-date systematic review and meta-analysis to investigate the effect of SARS-CoV-2 infection on sperm quality in COVID-19-positive and COVID-19-negative male participants. Method . Seven databases were searched for literature released through June 10, 2022, containing estimates for the outcomes of interest. Using a random-effects model (REM) or a fixed-effects model (FEM), we analyzed the pooled results. The quality of all included studies was assessed by the Newcastle-Ottawa scale. In addition, we performed a quantitative and subgroup analysis of semen data across all included studies. Results . Fourteen studies were extracted from 10 publications, involving a total of 1174 participates for meta-analysis. Sperm parameters of 521 COVID-19 male patients and 653 controls were analyzed. In 8 case-control studies, the pooled mean difference (MD) of total sperm motility was -5.37% (95% confidence interval (CI): -8.47 to -2.28; p < 0.05 ), suggesting that total motility was significantly impaired in male COVID-19 cases. Subgroup analysis showed a significant decrease in semen volume, sperm concentration, and total motility in 238 patients with a recovery time of less than 90 days. Moreover, in the other 6 included pre- to post-COVID-19 studies, the pooled MDs of sperm concentration, total sperm count, total motility, progressive motility, and normal morphology were − 6.54 × 10 6 /ml (95% CI: -10.27 to -2.81; p < 0.05 ), − 38.89 × 10 6 (95% CI: -59.20 to -18.58; p < 0.05 ), -7.21% (95% CI: -14.36 to -0.07; p < 0.05 ), -5.12% (95% CI: -8.71 to -1.53; p < 0.05 ), and -1.52% (95% CI: -2.88 to -0.16; p < 0.05 ), respectively, which indicate SARS-CoV-2 infection significantly affected these five sperm parameters. Conclusion . Our results revealed that SARS-CoV-2 infection was significantly correlated with decreased sperm quality. Of six sperm parameters, total motility and sperm concentration were the most significantly decreased parameters. These results suggest a possible negative influence of SARS-CoV-2 infection on testicular function and male fertility. Given the potential detrimental effect of SARS-CoV-2 on semen quality, male reproductive health should be monitored closely in patients with COVID-19. This trial is registered with CRD42021275823 .
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.011 | 0.009 |
| 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) | 0.005 | 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".