The impact of Covid-19 vaccines on fertility-A systematic review and metanalysis
Bibliographic record
Abstract
Abstract Introduction Despite literature’s proofs about their safety, concerns arose regarding adverse events due to Covid-19 vaccines, including the possible impact on fertility, accentuated by misinformation and anti-vaccine campaigns. The aim of this study was to evaluate the Covid-19 vaccines’ impact on male and female fertility. Methods PubMed, Scopus, Web of Science, Cochrane and Embase databases were searched for eligible studies until March 7th, 2022. Primary studies investigating the Covid-19 vaccines impact on male and female fertility, were included. Studies’ quality was assessed by the Newcastle-Ottawa and the Before and After Quality Assessment scales for cohort and pre-post studies, respectively. Random-effect meta-analyses were performed for parameters considered in ≥ 2 studies, calculating means, p-values and 95% Confidence Intervals (CIs). I2 statistics was used to assess statistical heterogeneity. Results Out of 1406 studies screened, 20 studies were included in the systematic review. These studies, conducted in Israel (35%), USA (30%), Russia (25%), China (5%) and Italy (5%), were of poor (15%), moderate (75%) and good (10%) quality. Meta-analyses among five studies considering several vaccines were performed for pre- and post-vaccination sperm progressive motility ((49%, 95% CI 36-67% vs 49%, 95% CI 39-61%; p = 0.963) and concentration (64.39 mln/ml, 95% CI 47.51-87.28 and 72.00 mln/ml, 95% CI 51.22-101.21; p = 0.03). Subgroup meta-analyses based on the type of vaccine showed no significant difference: between vaccinated with mRNA vaccines and non-vaccinated regarding biochemical pregnancy rates; pre- and post-vaccination with Gam-COVID-Vac regarding testosterone, FSH and LH levels; pre- and post-vaccination with BNT162b2 vaccines regarding sperm volumes. Discussion There is no scientific proof of any association between Covid-19 vaccines and infertility in men or women. Misinformation and doubts about vaccines should be properly addressed. Key messages • The doubts regarding Covid-19 vaccines’ impact on both male and female fertility resulted to be unfounded. Covid-19 vaccines remain the most important weapon to fight the pandemic. • It is important to keep providing to public opinion and health care providers evidence-based scientific information, in order to effectively combat misinformation and anti-vaccines campaigns.
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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.012 | 0.010 |
| 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".