IgA Vasculitis With Underlying Liver Cirrhosis: A French Nationwide Case Series of 20 Patients
Bibliographic record
Abstract
Objective Immunoglobulin A vasculitis (IgAV) and nephropathy (IgAN) share common immunological mechanisms. Liver cirrhosis is well known to be associated with IgAN. Here, we aimed to describe the presentation and outcome of IgAV patients with underlying cirrhosis. Methods We conducted a French nationwide retrospective study of adult patients presenting with both IgAV and cirrhosis. Baseline characteristics were compared to those of the 260 patients included in the French nationwide IgAV registry (IGAVAS). Results Twenty patients were included, and 7 (35%) were female. The mean ± SD age was 62.7 ± 11 years. At baseline, compared with IGAVAS patients, patients with underlying cirrhosis were older (62.7 ± 11 vs 50.1 ± 18, P < 0.01) and displayed more constitutional symptoms (weight loss 25% vs 8%, P = 0.03). Patients with underlying cirrhosis were also more likely to exhibit elevated serum IgA levels (5.6 g/L vs 3.6 g/L, P = 0.02). Cirrhosis and IgAV were diagnosed simultaneously in 12 patients (60%). Cirrhosis was mainly related to alcohol intake (n = 15, 75%), followed by nonalcoholic steato-hepatitis (n = 2), chronic viral hepatitis (n = 1), hemochromatosis (n = 1), and autoimmune hepatitis (n = 1). During follow-up with a median of 17 months (IQR 12–84), 10/13 (77%) exhibited IgAV remission at Month 3. One patient presented a minor relapse. Six patients died, but no deaths were related to IgAV. Conclusion We report the first case series of IgAV patients with underlining cirrhosis, to our knowledge, which was mainly alcohol related. The liver disease did not seem to affect baseline vasculitis characteristics. Physicians should investigate the existence of liver cirrhosis at IgAV diagnosis, especially in the context of alcohol abuse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".