Risk factors for recurrence of autoimmune hepatitis after liver transplantation
Bibliographic record
Abstract
Autoimmune hepatitis has been reported to recur after liver transplantation. The aim of our study was to evaluate the risk factors associated with recurrence of autoimmune hepatitis. Forty-six patients that underwent liver transplantation because of end-stage liver disease secondary to autoimmune hepatitis were studied. Recurrence of autoimmune hepatitis was diagnosed in 11 of the 46 (24%) patients, and the overall 5-year probability of recurrence was 18%. By univariate Cox analysis, the features before liver transplantation associated with a higher risk of recurrence were concomitant autoimmune disease [hazard ratio (HR), 3.74; 95% confidence interval (CI), 1.05-13.36; P = 0.04], high aspartate aminotransferase (HR, 1.09; 95% CI, 1.03-1.14; P = 0.002), high alanine aminotransferase (HR, 1.09; 95% CI, 1.03-1.20; P = 0.003), and high immunoglobulin G (IgG; HR, 1.25; 95% CI, 1.11-1.41; P = 0.0003). Moreover, patients with recurrence had a higher frequency of moderate to severe inflammatory activity (HR, 5.3; 95% CI, 1.55-18.79; P = 0.008) and plasma cell infiltration in the liver explant (HR, 5.8; 95% CI, 1.52-22.43; P = 0.01). In the multivariate Cox analysis, only the presence of moderate to severe inflammation (HR, 6.9; 95% CI, 1.76-26.96; P = 0.006) and high IgG levels before liver transplantation (HR, 7.5; 95% CI, 1.45-38.45; P = 0.02) were independently associated with the risk of autoimmune hepatitis recurrence. In conclusion, patients with concomitant autoimmune disease, high aspartate aminotransferase, alanine aminotransferase, and IgG before the transplant, or moderate to severe inflammatory activity or plasma cell infiltration in the liver explant have a higher risk of recurrent disease. These findings suggest that recurrence of autoimmune hepatitis may reflect incomplete suppression of disease activity prior to liver transplantation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".