Maternal Influenza Immunization and Adverse Birth Outcomes: Using Data and Practice to Inform Theory and Research Design
Bibliographic record
Abstract
Maternal influenza immunization can reduce influenza-attributable morbidity and mortality among pregnant women and infants who are too young to be vaccinated. Data from empirical studies also support the hypothesis that immunization can protect the fetus against adverse outcomes if the mother is exposed to influenza. In their theoretical analysis in the Journal, Hutcheon et al. (Am J Epidemiol 2016;184(3):227-232) critiqued the existing evidence of the fetal benefits of maternal influenza immunization by calculating the sample sizes needed to demonstrate hypothetical reductions in risk and concluded that the benefits observed in empirical studies are likely implausible. However, in their analysis, they did not take into account multiple fundamental characteristics of influenza epidemiology, including the time-variable effects of influenza illness and vaccination during pregnancy, or well-known differences in disease epidemiology between seasons, populations, and geographic regions. Although these and other factors might affect the magnitude of fetal benefit conferred by maternal influenza immunization, studies in which investigators have accounted for influenza circulation have demonstrated a consistent protective effect against a variety of adverse birth outcomes; those studies include the only randomized controlled trial designed a priori and adequately powered to do so. Only a comprehensive and nuanced assessment of the evidence base will allow for effective translation of these data into a global immunization policy.
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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 | 0.253 | 0.613 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.034 | 0.041 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".