Vaccination during pregnancy: A golden opportunity to embrace
Bibliographic record
Abstract
Immunization strategies are part of routine pregnancy care to prevent infectious diseases in the mother, the fetus, and the newborn. Maternal immunization recommendations followed the recognition of the consequences of infectious diseases in pregnancy, including vertical transmission and perinatal consequences. The recent COVID-19 pandemic highlighted the issue of vaccination among pregnant individuals. Recommendations vary globally; however, Tdap, influenza, and, recently, COVID-19 vaccines are routinely recommended during pregnancy. There are several new maternal immunization products in the pipeline, including those directed against malaria, cytomegalovirus, Group B Streptococcus, herpes simplex virus, and respiratory syncytial virus. Important challenges must be addressed in all countries to guarantee that pregnant individuals and their babies receive the best care possible, including uptake of recommended immunizations by their entire target population groups. These challenges include disseminating appropriate data for vaccine recommendations and many others, such as ensuring stakeholder endorsement, achieving in-country distribution and administration, adequate vaccine supply, and a well-organized healthcare system, ideally offering the immunization free of charge. More recently, the hesitancy of pregnant women to receive immunizations highlights the relevance of cultural aspects and other contextual factors affecting vaccine uptake among pregnant individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".