Adverse event following immunization surveillance systems for pregnant women and their infants: a systematic review
Bibliographic record
Abstract
BACKGROUND: The World Health Organization's Strategic Advisory Group of Experts on Immunization has declared that maternal immunization is a key priority. Robust adverse event following immunization (AEFI) surveillance systems that capture outcomes in pregnant women and their infants are needed to ensure the safety of maternal immunization programs. We sought to identify the active and passive AEFI surveillance systems for pregnant women and their offspring described in the literature. METHODS: A systematic literature review was conducted of the MEDLINE, CINAHL, and EMBASE databases from 1990 to 2014. English-language articles were reviewed if they included pregnant women as the population of interest and described the surveillance method used. RESULTS: Of 619 articles retrieved from the search, 16 met the criteria for review. These included reports of AEFI surveillance for pregnant women, their offspring, or both. The majority of reports (11/16) came from the USA and described findings on two active and four passive AEFI surveillance systems, only three of which specifically targeted pregnant women. The remaining five articles described one-time AEFI surveillance programs, all in high-income countries. CONCLUSION: There are no published reports outside of the USA of ongoing AEFI surveillance systems that specifically target pregnant women or their offspring. There may be AEFI surveillance systems that capture events in these populations that have not been reported in the literature. A survey of immunization program managers and national regulatory authorities is needed to determine the current status of AEFI surveillance for pregnant women and their offspring globally.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".