A Systematic Review of Histopathologic Surveys on Mucocutaneous Biopsies in Patients Developed COVID-19 Vaccine-Related Dermatologic Manifestations
Bibliographic record
Abstract
ABSTRACT: Coronavirus 2 is an infectious agent primarily identified as the cause of a pandemic viral pneumonia. With the mass vaccination against this virus, one of the health issues is the safety of currently available vaccines considering their adverse reactions. This systematic review was conducted to assess and summarize all reported data on histopathologic findings associated with mucocutaneous reactions that developed after COVID-19 vaccination for a better pathophysiology interpretation and clinical management of these reactions. A systematic search was performed in PubMed, Web of Science, and Scopus databases as well as Google Scholar engine for relevant English articles published till July 1, 2022. This review includes 131 studies with a total number of 287 cases. Eruptions that underwent a biopsy were mostly described as erythematous maculopapular, papulosquamous, vasculitis-like, lichenoid, or urticarial lesions. Histopathology revealed spongiosis, interstitial, and perivascular lymphohistiocytic infiltration, erythrocyte extravasation, parakeratosis, endothelial inflammation, and the like. Findings were highly consistent with morbilliform erythema, psoriasiform dermatosis, leukocytoclastic vasculitis, and lichenoid or urticarial drug reactions. The majority of these reactions had a mild nature and were primarily observed in patients with underlying health conditions. Microscopic evaluation was also consistent with transient inflammatory changes, and features like neutrophilic infiltrates, subcorneal pustules, and vasculopathy were less frequently reported than what seen in COVID infection. Therefore, dermatologic reactions developing after vaccination in the general population should not hinder a complete vaccination.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".