The Impact of COVID‐19 Vaccines on Male Semen Parameters: A Retrospective Cohort Study
Bibliographic record
Abstract
The emergence of SARS‐CoV‐2 and the subsequent COVID‐19 pandemic necessitated the development of adequate vaccines. Despite vaccines being demonstrated to be safe and effective for preventing severe disease and death, vaccine hesitancy remains. Reasons include concerns over adverse effects on male fertility, which have not been widely investigated. Therefore, this study is aimed at determining the impact of COVID‐19 vaccination on semen parameters in a retrospective cohort study of South African males undergoing fertility assessment. The patients for this study were adult men who have previously undergone routine semen analysis for fertility assessment at Androcryos Andrology Laboratory (Johannesburg, South Africa) between March 2021 and March 2022. They also received vaccination within 3 months following a semen analysis and underwent a second semen analysis any time post‐COVID‐19 vaccination. From 277 records analysed, 46 patients met the inclusion criteria, receiving the Pfizer‐BioNTech (BNT162b1) (63%), Johnson and Johnson (JNJ‐78436735/Ad26.COV2S) (34.8%), and the AstraZeneca (AZD1222) (2.2%) vaccines. Sperm concentration significantly increased postvaccination (P = 0.0001), with no significant changes in semen pH, volume, total sperm count, progressive motility, normal sperm morphology, or chromatin condensation. Results were not influenced by age, type of vaccine received, and the number of days following vaccination, as depicted by multiple regression analysis. In conclusion, there is no evidence of a negative impact of COVID‐19 vaccination on male semen parameters, which is consistent with the emerging literature on COVID‐19 vaccination and male fertility. COVID‐19 vaccinations should not be dismissed based on fear of adverse effects on male fertility parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".