COVID-19 Vaccination as a Trigger of IgA Vasculitis: A Global Pharmacovigilance Study
Bibliographic record
Abstract
OBJECTIVE: IgA vasculitis (IgAV) can occur after vaccination. We aimed to assess a potential safety signal on the association between coronavirus disease 2019 (COVID-19) vaccines and IgAV. METHODS: Cases of IgAV involving COVID-19 vaccines were retrieved in VigiBase. Disproportionate reporting was assessed using the Bayesian information component (IC) with all other drugs and vaccines as control groups. RESULTS: Three hundred thirty patients with de novo IgAV from 24 countries were included, mostly from the United States (193/330, 58%). Fifty percent (163/328) were female and median age was 32 years (IQR 15-59), of which 33% (84/254) were young (1-17 yrs). Median time to onset of IgAV was 7 days (IQR 2-16; n = 256) and 85% (280/330) of patients were vaccinated with mRNA vaccines. Seriousness was reported in 188/324 (58%) cases. Sixty-five percent (95/147) recovered and 1% (2/147) died. A positive rechallenge was reported for 3 of 4 patients (75%). A total of 996 cases of IgAV were identified with other vaccines. There was a small significant increase in IgAV reporting with COVID-19 vaccines compared with all other drugs (IC 0.22, 95% credible interval [CrI] 0.04 to 0.35). No disproportionality signal was found between COVID-19 vaccines and other vaccines (IC -1.42, 95% CrI -1.60 to -1.28). There was no significant difference between mRNA vaccines and viral vector COVID-19 vaccines. Men and children had a significant overreporting of IgAV compared with women and adults, respectively. CONCLUSION: This study provides reassuring results regarding the safety of COVID-19 vaccines in the occurrence of IgAV compared to other vaccines.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".