Vasculitis as Temporally Associated With COVID-19 Infection or Vaccination: A Single-center Experience
Bibliographic record
Abstract
To the Editor: Vasculitis has been recognized as an organ-specific immune-mediated complication of the SARS-CoV-2 infection, and the number of reported coronavirus disease 2019 (COVD-19)–associated vasculitis cases is gradually increasing.1 Vasculitis can develop early after the onset of COVID-19 (an interval of < 2 weeks) or manifest later during the course of the disease, and it is associated with a significant morbidity. The recent review of 19 vasculitic cases following COVID-19 reported the need for intensive care treatment in 31% and death in 16% of cases.1 Lacking specific antiviral treatment, the best strategy to stop the pandemic currently relies on timely and adequate immunization worldwide. COVID-19 vaccines proved to be efficacious and safe in the registration studies, their benefits largely outweighing the risk of rare potential complications.2 Nevertheless, immunization may induce de novo autoimmune diseases, particularly in genetically predisposed individuals. Indeed, reports of vasculitis (most commonly cutaneous vasculitis and IgA vasculitis) following vaccination (most frequently influenza vaccine) have been … Address correspondence to Dr. A. Hočevar, University Medical Centre Ljubljana, Department of Rheumatology, Vodnikova cesta 62, 1000 Ljubljana, Slovenia. Email: alojzija.hocevar{at}gmail.com.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 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".