An Assessment of the Reporting Pattern of Adverse Events Following Immunizations in VigiAccess
Bibliographic record
Abstract
PURPOSE: Globally, adverse events following immunization (AEFI) reporting continues to be a challenge. It is estimated that about 95% of AEFIs never get reported after vaccinations necessitating strategies to improve it. The introduction of databases such as VigiAccess in which AEFI data from Pharmacovigilance centres around the world can be assessed is an important step towards improving AEFI reporting and enhancing vaccine safety. This study assessed the reporting pattern of AEFIs from the various continents of the world in VigiAccess, an open-access pharmacovigilance database. METHODS: VigiAccess was thoroughly searched on the 5th of February 2018 for the categories of reported AEFIs and number and types of AEFIs reported for measles vaccine, oral polio vaccine, yellow fever vaccine, pneumococcal vaccine, rotavirus vaccine, meningococcal vaccine, tetanus vaccine and BCG vaccine. RESULTS: After a thorough search through VigiAccess, 27 categories of reported AEFIs were retrieved. The total number of AEFIs for the 8 vaccines was 813,973. General disorders and administration site conditions were the highest number of AEFIs (251,405 representing 30.9%) followed by skin and subcutaneous tissue disorders (93,011 representing 11.4%) and nervous system disorders (89,077 representing 10.9%). With the continental data, the Americas recorded the highest number of AEFIs followed by Europe, Oceania, Asia and Africa. CONCLUSION: General and vaccine administration site conditions were the highest number of AEFIs. The Americas recorded the highest number of AEFIs whereas Africa recorded the least. VigiAccess needs improvement in data synchronization to enhance its reliability.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".