Current situation of vaccine injury compensation program and a future perspective in light of COVID-19 and emerging viral diseases
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> Vaccines have had a great impact on disease prevention and reducing mortality. Very rarely, vaccines also can result in serious adverse effects. In consideration of this fact, vaccine injury compensation programs have been implemented in many countries to compensate a vaccinee for associated adverse effects. The existing vaccine injury compensation system addresses routine immunization schemes. However, there are rising concerns about the compensation for adverse effects caused by new vaccines such as those developed for coronavirus disease 2019 (COVID-19). This review focuses on vaccine injury compensation programs and highlights the necessity to include all upcoming new vaccines for COVID-19 and other emerging viral diseases in the compensation schemes. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> Published articles relating to vaccine compensation injury programs, vaccines, injuries, disabilities, illnesses, and deaths resulting from vaccination were searched in data bases. Through a careful review of the abstracts, 25 relevant articles were selected for analysis. </ns3:p> <ns3:p> <ns3:bold>Results</ns3:bold> : We identified 27 countries on four continents with vaccine injury compensation schemes: 17 countries in Europe, 7 countries in Asia, the United States, a Canadian Province and New Zealand. No programs were identified in Africa and in South America. Program design, funding, and eligibility for compensation vary vastly between countries. We identified 17 countries operating well-established vaccine injury compensation programs. However, minimal information is available on numerous other countries. </ns3:p> <ns3:p> <ns3:bold>Conclusion</ns3:bold> : We conclude that the vaccine injury compensation programs are available in limited number of countries across four continents - mostly in Europe. Lack of standard approach and scope of injury prevention and compensation programs across the countries exists. Some important limitations include limited scientific material, which hindered our research. Therefore, additional data concerning payout for each type of injury and the number of claimants related to a specific vaccine injury worldwide could provide a more comprehensive analysis. </ns3:p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".