Alterations in Human Semen After Infection with SARS-CoV-2: A Meta-analysis
Bibliographic record
Abstract
Background: The purpose of the current study was to assess pooled prevalence (PP) of SARS-CoV-2 in semen and pooled estimates including weighted mean difference (WMD) and risk ratio (RR) of semen characteristics in infected cases as compared with healthy controls. Methods: Major databases were searched by two authors. SARS-CoV-2-positive cases were assigned to the exposed arm (group A), whereas the controls to the unex-posed (group B). Risk of bias was assessed with Newcastle-Ottawa Scale and PRISMA guidelines were followed. Random-effects model was employed for analyzing the heterogeneity and fixed-effects model for homogeneity of studies. Results: Of 170 studies, 14 studies were eligible involving 507 subjects (316 in group A, 191 in group B). The risk of bias was the highest for "comparability" domain. SARS-CoV-2 RNA was found in only two studies among 7 subjects (PP= 2.10%, 95%CI 0.58-4.42). There was a significant decrease in sperm concentration (WMD= -15.29, 95%CI -24.70 - -5.88) and total sperm in ejaculate (WMD= -47.58, 95%CI -86.40 - -8.75) in group A. The effect of COVID-19 upon progressive motility, ejaculate volume, and leukocyte presence in semen was not significant. Conclusion: Prevalence of SARS-CoV-2 in semen among the infected cases is low. Sexual transmission through semen is improbable and of little concern for public health. Sperm concentration and total sperm in ejaculate are significantly reduced as compared with controls. Due to limited information of the current research, longer follow-up is needed to identify delayed or progressive impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.051 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".