Vaccine adverse events reporting system globally
Bibliographic record
Abstract
Worldwide reporting of vaccines adverse event following immunization is a challenging problem because of absence of well framed reporting system in maximum countries of the world. In Past ten years fear of disease like polio, measles, tetanus, diphtheria etc was important concern in the mind of both parents & doctors than adverse reactions of the vaccinations, but today with the availability of effective vaccines and associated adverse reaction, lead to change in the thinking towards safe use of vaccine through vaccine pharmacovigilance. Hence there is need of effective vaccine adverse reaction reporting system in countries to find rare, serious adverse event following vaccination. This article reviews on vaccine adverse event reporting Systems (offline or /& online reporting) in different countries like U.S.A, U.K, Australia, Singapore, India, Newzealand, Saudi, Srilanka, Canada that have a somewhat effective vaccine phamacovigilance system and also focuses on efforts of WHO in enhancing reporting of adverse event following immunization globally. Death or any type of harm to pediatric population can only be minimized if serious to non serious ADRs be reported with joint effort of health care professionals including doctors, nurses, pharmacist and consumer.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.046 |
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".