Drug-associated Cutaneous Vasculitis: Study of 239 Patients from a Single Referral Center
Bibliographic record
Abstract
OBJECTIVE: The 2012 International Chapel Hill Consensus Conference on the Nomenclature of Vasculitides defined drug-associated immune complex vasculitis as a distinct entity included within the category of vasculitis associated with probable etiology. In the present study we assessed the clinical spectrum of patients with drug-associated cutaneous vasculitis (DACV). METHODS: Case records were reviewed of patients with DACV treated at a tertiary referral hospital over a 36-year period. A diagnosis of DACV was considered if the drug was taken within a week before the onset of the disease. RESULTS: From a series of 773 unselected cutaneous vasculitis cases, 239 patients (30.9%; 133 men and 106 women; mean age 36 yrs) were diagnosed with DACV. Antibiotics (n=149; 62.3%), mainly β-lactams and nonsteroidal antiinflammatory drugs (NSAID; n=24; 10%) were the most common drugs. Besides skin lesions (100%), the most common clinical features were joint (51%) and gastrointestinal (38.1%) manifestations, nephropathy (34.7%), and fever (23.8%). The most remarkable laboratory data were increased erythrocyte sedimentation rate (40.2%), presence of serum cryoglobulins (26%), leukocytosis (24.7%), positive antinuclear antibodies (21.1%), anemia (18.8%), and positive rheumatoid factor (17.5%). Despite drug discontinuation and bed rest, 108 patients (45.2%) required medical treatment, mainly corticosteroids (n=71) or immunosuppressive drugs (n=7). After a median followup of 5 months, relapses occurred in 18.4% of patients, and persistent microhematuria or renal insufficiency in 3.3% and 5%, respectively. CONCLUSION: DACV is generally associated with antibiotics and NSAID. In most cases it has a favorable prognosis, although a small percentage of patients may develop residual renal damage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".