Vaccination from the early second trimester onwards gives a robust SARS-CoV-2 antibody response throughout pregnancy and provides antibodies for the neonate
Bibliographic record
Abstract
OBJECTIVES: Preventive measures against COVID-19 are essential for pregnant women. Pregnant women are particularly vulnerable to emerging infectious pathogens due to alterations in their physiology. We aimed to determine the optimum timing of vaccination to protect pregnant women and their neonates from COVID-19. METHODS: A prospective observational longitudinal cohort study in pregnant women who received COVID-19 vaccination. We collected blood samples to evaluate levels of antispike, receptor binding domain and nucleocapsid antibodies against SARS-CoV-2 before vaccination and 15 days after the first and second vaccination. We determined the neutralizing antibodies from mother-infant dyads in maternal and umbilical cord blood at birth. If available, immunoglobulin A was measured in human milk. RESULTS: We included 178 pregnant women. Median antispike immunoglobulin G levels increased significantly from 1.8 to 5431 binding antibody units/ml and receptor binding domain from 6 to 4466 binding antibody units/ml. Virus neutralization showed similar results between different weeks of gestation at vaccination (P >0.3). CONCLUSION: We advise vaccination in the early second trimester of pregnancy for the optimum balance between the maternal antibody response and placental antibody transfer to the neonate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".