Antineutrophil Cytoplasmic Antibody (ANCA)-Associated Renal Vasculitis Following COVID-19 Vaccination: A Case Report and Literature Review
Bibliographic record
Abstract
Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is an immune-mediated disorder of small and medium-sized vessels, characterized by the production of autoantibodies that target the neutrophilic antigens leading to mononuclear cell infiltration and destruction of blood vessels in lungs, skin, and kidneys. Although rare, the coronavirus disease 2019 (COVID-19) vaccine may trigger autoimmune vasculitis. We report a rare case of ANCA-associated renal vasculitis following COVID-19 vaccination in a 59-year-old male who presented with flu-like symptoms and deranged renal function tests. He received his second dose of the Pfizer COVID-19 vaccine 17 days ago. His clinical picture, serological testing, and radiological imaging were concerned with glomerular disease. His serum was positive for ANCAs, and the renal biopsy specimen revealed pauci-immune glomerulonephritis. He was diagnosed with AAV-associated renal vasculitis following COVID-19 vaccination because no other etiology was identified. His clinical improvement after starting rituximab and steroids reinforced the diagnosis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".