A Systematic Review of Vascular Injuries: A Review of Petechiae, Purpura, and Ecchymosis in Critical Situations Following COVID‐19 Vaccination
Bibliographic record
Abstract
Background and Aims: Vascular injuries characterized by petechiae, purpura, and ecchymosis have been reported as potential adverse effects following COVID-19 vaccination. This study aims to identify the characteristics of patients experiencing vascular injuries postvaccination and to outline key clinical considerations. Methods: A systematic review was conducted in accordance with PRISMA guidelines. A comprehensive search of Scopus, Web of Science, and PubMed/MEDLINE databases was performed for English-language publications up to July 2024. Eligible studies included reports of vascular injuries following COVID-19 vaccination. Results: Of the 1064 articles retrieved, 35 studies met the inclusion criteria. The majority of cases presented symptoms after receiving the first doses of Pfizer-BioNTech, Moderna, AstraZeneca, and Janssen vaccines. Diagnosed conditions included thrombotic thrombocytopenic purpura (TTP), immune thrombocytopenic purpura (ITP), vasculitis, and acquired hemophilia A. None of the patients tested positive for SARS-CoV-2 at the time of diagnosis. The most commonly affected sites were the lower extremities, with petechiae, purpura, and ecchymosis being the predominant manifestations. Conclusion: Our findings suggest a possible but unconfirmed association between COVID-19 vaccination and the development of vascular injuries, including petechiae, purpura, and ecchymosis. These symptoms may serve as early indicators of critical conditions requiring urgent medical intervention. Further research and postvaccination monitoring are necessary to establish causality and assess potential risk factors.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".