Systemic Vasculitis in Patients with Hepatitis C Virus Infection with and without Detectable Mixed Cryoglobulinemia
Bibliographic record
Abstract
OBJECTIVE: to describe hepatitis C virus (HCV)-related systemic vasculitis in patients without detectable mixed cryoglobulinemia (MC) and to compare them to typical cases of HCV-MC vasculitis. METHODS: twelve HCV RNA+ patients with histologically proven vasculitis in the absence of detectable MC (cases) were retrospectively compared with 48 HCV RNA+ patients with MC vasculitis (controls). Each case was matched with 4 controls for age and sex. RESULTS: the main epidemiological and virologic features were similar between cases and controls. No clinical difference was found, except for lower rates of arthralgias (33% vs 71%; p = 0.02) and purpura (50% vs 83%; p = 0.03) in cases. Cases showed higher mean serum C3 (1.17 ± 0.21 vs 0.93 ± 0.23 g/l; p = 0.01) and median C4 levels (0.25 vs 0.04 g/l; p < 0.001), lower median serum IgM levels (0.6 vs 1.9 g/l; p < 0.001), and lower rates of rheumatoid factor positivity (8% vs 82%; p < 0.001) than controls. The main histologic features were similar between cases and controls. Immunofluorescence analysis of skin biopsy from 1 case revealed perivascular deposits of C3 and IgA. After treatment, overall clinical response of vasculitis (75% vs 83%) and sustained virological response (40% vs 64%; p = 0.3) were similar between cases and controls, except for higher complete clinical response (42% vs 73%; p = 0.05) in controls. CONCLUSION: HCV-related systemic vasculitis may occur in the absence of detectable MC. Our findings suggest that such vasculitis probably results from immune complex-mediated mechanisms, and that the therapeutic management of such vasculitis should be similar to that of HCV-MC vasculitis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".