A systematic review of rubella vaccination strategies : impact on rubella and CRS incidence
Bibliographic record
Abstract
Objective: To evaluate the impact of rubella vaccination strategies on rubella and CRS incidence and to offer public health suggestions for vaccination strategy in China. \nMethods: A systematic research of studies was conducted via the United States National Library of Medicine and the National Institutes of Health Medical Database (PubMed) and Google Scholar database. Terms- rubella AND (vaccin* AND coverage AND (reproduct* OR transmiss*) were used as key words in the research via PubMed. “Rubella vaccination strategies” and “rubella and congenital rubella syndrome” and “incidence” were used to search via Google Scholar. A systematic review of literature was conducted. The effectiveness of rubella vaccination strategies on the control and elimination of rubella and CRS incidence was used as primary inclusion criteria. \nResults: 5 articles were selected according to inclusion criteria. They were two cohort studies, two ecological studies, one mathematical modeling study. From the experience in Canada, around 99% decline of rubella and CRS incidence happened after vaccination strategy with selective approach for children. However, it led to a shift of infection to susceptible groups, and couldn’t interrupt rubella virus circulation. A combined vaccination with adults could control CRS incidence but was not effective for CRS elimination. A vaccination strategy with universal approach for children, males and females in Brazil could prevent child-bearing women from re-infection, thus helped to eliminate CRS cases. \nConclusion: Vaccination strategy with universal approach was most effective for CRS control in a short time period with the evidence of a huge reduction of rubella and CRS incidence. As part of national immunization program in China, routine vaccination strategy for rubella should consider both epidemic and demographic factors to assess the effectiveness of children vaccination strategy.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".