Early Childhood Health Outcomes Following In Utero Exposure to Influenza Vaccines: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Vaccination during pregnancy is an effective strategy for preventing infant disease; however, little is known about early childhood health after maternal vaccination. OBJECTIVES: To systematically review the literature on early childhood health associated with exposure to influenza vaccines in utero. DATA SOURCES: We searched CINAHL Plus, Embase, Medline, Scopus, and Web of Science for relevant articles published from inception to July 24, 2019. STUDY SELECTION: We included studies published in English reporting original data with measurement of in utero exposure to influenza vaccines and health outcomes among children <5 years of age. DATA EXTRACTION: Two authors independently assessed eligibility and extracted data on study design, setting, population, vaccines, outcomes, and results. RESULTS: The search yielded 3647 records, of which 9 studies met the inclusion criteria. Studies examined infectious, atopic, autoimmune, and neurodevelopmental outcomes, and all-cause morbidity and mortality. Authors of 2 studies reported an inverse association between pandemic influenza vaccination and upper respiratory tract infections, gastrointestinal infections, and all-cause hospitalizations; and authors of 2 studies reported modest increased association between several childhood disorders and pandemic or seasonal influenza vaccination, which, after adjusting for confounding and multiple comparisons, were not statistically significant. LIMITATIONS: Given the small number of studies addressing similarly defined outcomes, meta-analyses were deemed not possible. CONCLUSIONS: Results from the few studies in which researchers have examined outcomes in children older than 6 months of age did not identify an association between exposure to influenza vaccines in utero and adverse childhood health outcomes.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".