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 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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".