Diagnostic accuracy of vaccine and vaccine excipient testing in the setting of allergic reactions to <scp>COVID</scp>‐19 vaccines: A systematic review and meta‐analysis
Bibliographic record
Abstract
For persons with immediate allergic reactions to mRNA COVID-19 vaccines, skin testing (ST) to the vaccine/excipients (polyethylene glycol[PEG] and polysorbate 80 [PS]) has been recommended, but has unknown accuracy. To assess vaccine/excipient ST accuracy in predicting all-severity immediate allergic reactions upon re-vaccination, systematic review was performed searching Medline, EMBASE, Web of Science, and the WHO global coronavirus database (inception-Oct 4, 2021) for studies addressing immediate (≤4 h post-vaccination) all-severity allergic reactions to 2nd mRNA COVID-19 vaccination in persons with 1st dose immediate allergic reactions. Cases evaluating delayed reactions, change of vaccine platform, or revaccination without vaccine/excipient ST were excluded. Meta-analysis of diagnostic testing accuracy was performed using Bayesian methods. The GRADE approach evaluated certainty of the evidence, and QUADAS-2 assessed risk of bias. Among 20 studies of mRNA COVID-19 first dose vaccine reactions, 317 individuals underwent 578 ST to any one or combination of vaccine, PEG, or PS, and were re-vaccinated with the same vaccine. Test sensitivity for either mRNA vaccine was 0.2 (95%CrI 0.01-0.52) and specificity 0.97 (95%CrI 0.9-1). PEG test sensitivity was 0.02 (95%CrI 0.00-0.07) and specificity 0.99 (95%CrI 0.96-1). PS test sensitivity was 0.03 (95%CrI 0.00-0.0.11) and specificity 0.97 (95%CrI 0.91-1). Combined for use of any of the 3 testing agents, sensitivity was 0.03 (95%CrI 0.00-0.08) and specificity was 0.98 (95%CrI 0.95-1.00). Certainty of evidence was moderate. ST has low sensitivity but high specificity in predicting all-severity repeat immediate allergic reactions to the same agent, among persons with 1st dose immediate allergic reactions to mRNA COVID-19 vaccines. mRNA COVID-19 vaccine or excipient ST has limited risk assessment utility.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".