Bibliographic record
Abstract
Vaccines require higher safety standards than most other medicinal products because they are given to healthy individuals, including infants, children, and elderly. Despite various activities by national agencies, public concern about vaccine safety often arises. Post-marketing activities for vaccine safety can be broadly classified into passive and active surveillances. Many countries as well as Korea operate passive vaccine safety surveillance systems that report adverse events related to vaccines. However, the active surveillance systems operate only in several countries, such as the United States of Amer ica (USA), Europe, Canada and Australia. In the US, Vaccine Safety Datalink (VSD) and Post-Licensure Rapid Immunization Safety Monitoring (PRISM) were developed in 1990 and 2009 respectively for monitoring vaccine actively. In the case of Europe, the Vaccine Adverse Event Surveillance and Communica tion (VAESCO) consortium was launched in 2008. After the end of VAESCO, the Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) was organized to establish a vaccine benefit-risk monitoring framework in 2013. Canada has been operating a vaccine active monitoring sys tem known as the Canadian Immunization Monitoring Program, ACTive (IMPACT) since 1991. The objective of this review was to describe and compare background, databases, and analysis systems of various vaccine active surveillance systems in the US, Europe, and Canada. We described the examples of studies on the safety of influenza A (H1N1) vaccines carried out in each system. This review could help provide directions for the future development of the ideal active vaccine safety surveillance system in Korea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".