The risk of anaphylaxis behind authorized COVID-19 vaccines: a meta-analysis
Bibliographic record
Abstract
Abstract Background A serious allergic reaction that may occur in response to medical products is anaphylaxis, which potentially can lead to anaphylactic shock. In the light of recent COVID-19 pandemic, much public attention had been paid to the severe allergic reactions occurring after COVID-19 vaccination. Therefore, in our study we would like to investigate the risk of authorized COVID-19 vaccines to induce anaphylactic reaction, anaphylactoid reaction, anaphylactic shock and anaphylactoid shock. Methods We searched databases, such as PubMed, Web of Science and Embase and found eight articles about the incidence of anaphylactic and anaphylactoid reactions. Also, we used data from four databases from Canada, the U.S., the European Union and the United Kingdom. To calculate effect sizes, we used random effects model with inverse variance method. The risk ratio with 95% confidence interval were used for dichotomous outcomes. Statistical analysis was prepared in R. Results were considered statistically significant at p < 0.05. Results The most cases of anaphylactic reaction, anaphylactoid reaction, anaphylactic shock and anaphylactoid shock were reported in female aged 18–85 years after BNT162b2 vaccine according to data from the EU. Analyzed COVID-19 vaccines can cause the anaphylaxis/anaphylactic reaction with risk of 106.99 (95% CI [39.95; 286.57], p < 0.0001, I 2 = 59%), whereas the anaphylactoid reaction, anaphylactic and anaphylactoid shocks with risk of 113.3 (95% CI [28.11; 456.53], p < 0.0001), 344.2 (95% CI [85.77; 1381.39], p < 0.0001), 14.9, 95% CI [1.96; 112.79], p = 0.009), respectively. Conclusions Our meta-analysis shows that the risk of anaphylactic reaction, anaphylactoid reaction, anaphylactic shock and anaphylactoid shock do not occur only after mRNA COVID-19 vaccines. Therefore, vaccination centers should be prepared to render assistance in the event of a reaction in all cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".