Does SARS-CoV-2 Affect Human Semen? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Contradictory results have been reported regarding effects of the SARS-CoV-2 upon human semen. A timely and up-to-date systematic review with meta-analysis appears necessary. This study aimed to deliver pooled prevalence (PP) of SARS-CoV-2 in semen and pooled semen parameters as compared with the uninfected. The relevant databases were scanned by two authors for observational studies reporting analysis of semen in COVID-19 patients. The SARS-CoV-2-infected were assigned to group A (exposed arm), whereas the uninfected to group B (unexposed arm). Newcastle-Ottawa Scale was used to address the risk of bias. PRISMA guidelines were adopted. In case of homogenous studies, fixed-effects model was followed, whereas for heterogenous studies random-effects model was used. Of 990 studies, 24 were eligible involving 1589 subjects (947 in group A and 642 in group B). The "comparability" domain was biased the most. SARS-CoV-2 RNA was detected in three studies among 8 individuals producing the PP of 1.76% (95% CI 0.72-3.21). Sperm concentration was reduced significantly (WMD = -16.23 [95% CI -25.56 to -6.89], as well as total sperm in ejaculate (WMD = -34.84 [95% CI - 43.51 to -26.17]) and sperm volume (WMD = - 0.48 [95% CI - 0.59 to - 0.36] in group A as compared with controls. There was a non-significant effect upon progressive motility and leukocyte presence in semen. SARS-CoV-2 RNA in semen among the infected individuals is detected infrequently. By this token, sexual transmission through semen is of low probability and little concern for public health. However, significant decrease in sperm volume, sperm concentration, and total sperm in ejaculate has been noted. The current data, though, are limited, and more studies with longer follow-up are needed to evaluate the further impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".