SARS-CoV-2 Antibodies in Human Milk After mRNA and Adenovector-Based Vaccination: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: SARS-CoV-2 specific antibodies exist in human milk expressed by lactating parents after vaccination. In the existing research, the effects of vaccine types on human milk are inconsistent. RESEARCH AIM: This study aims to perform a systematic review and meta-analysis of the existing observational studies to compare the positive rates of SARS-CoV-2 specific antibodies in human milk according to mRNA and adenovector-based vaccination. METHODS: PubMed, Web of Science, Elsevier Science Direct and Cochrane Library databases were systematically searched for relevant articles published from December 30, 2019 to February 15, 2023. Observational studies were considered eligible provided they reported data on SARS-CoV-2 specific antibodies in human milk. The risk of bias in non-randomized studies of interventions (ROBINS-I) tool, the Newcastle-Ottawa Scale (NOS), and the Agency for Healthcare Research and Quality (AHRQ) were used to assess risk of bias. Seven studies, including 511 lactating participants, were included in this review and meta-analysis. RESULTS: < 0.001). The positive rate of SARS-CoV-2 IgG was higher in mRNA vaccines than in adenovector-based vaccines. CONCLUSIONS: Compared to adenovector-based vaccines, mRNA vaccines present a higher positivity rate of IgA and IgG in human milk after vaccination. In other words, mRNA vaccinations may offer breastfed children a higher level of protection than adenovector-based vaccinations. Further high-quality data is still required to substantiate these findings.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.004 | 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".