Successful mRNA COVID-19 vaccination in a patient with a history of severe polyethylene glycol anaphylaxis
Bibliographic record
Abstract
BACKGROUND: The mechanism of action behind anaphylactic reactions to the mRNA COVID-19 vaccines remains unknown, but the excipient polyethylene glycol, PEG-2000, has been implicated. Initial recommendations were made for excipient testing with PEG-3350 to help risk stratify individuals and identify an etiology. Here we present a case of a patient with a history of polyethylene glycol anaphylaxis and positive skin testing to PEG-3350, who successfully received both doses of the Pfizer-BioNTech COVID-19 mRNA vaccine in a single step with only premedication. CASE PRESENTATION: A 56-year-old man was referred to our clinic for assessment of his eligibility in receiving the COVID-19 vaccine given a history of anaphylaxis to PEG. He had two anaphylactic episodes: one in 2018 to methylprednisolone acetate intra-articular injection and one to oral PEG-3350 in 2020. Confirmatory skin prick testing was done in our clinic to PEG-3350 that was positive at 35 mm with appropriate positive and negative controls. Despite this he wanted to receive the PEG-containing mRNA COVID-19 vaccines and was counselled on the risks and benefits. He successfully received both doses of the Pfizer-BioNTech COVID-19 mRNA vaccine in a single step with only pre-treatment with Cetirizine 20 mg daily and Montelukast 10 mg daily for 5 days. CONCLUSIONS: In conclusion, our case demonstrates that a patient with a confirmed polyethylene glycol anaphylaxis could safely receive both doses of the COVID-19 mRNA vaccines in a single step with pre-treatment. We hope that our case will further support the limited role in skin testing to PEG in the assessment of COVID-19 mRNA vaccine allergy and highlight the need for further research to elucidate the mechanism of action behind these allergic reactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".