Incidence and risk factors associated with<i>de novo</i>autoimmune hepatitis after liver transplantation
Bibliographic record
Abstract
BACKGROUND/AIMS: De novo autoimmune hepatitis (AIH) describes the development of hepatitis with autoimmune features in liver transplant (LT) patients without prior diagnosis of AIH. We aimed to evaluate the incidence and risk factors for de novo AIH. METHODS: A cohort of 576 patients with LT for aetiologies other than AIH was evaluated. RESULTS: De novo AIH was diagnosed in 17 patients (3%) with an overall incidence of 4.0 cases per 1000 patient-years. By univariate Cox analysis, patients who received cyclosporine A had lower risk (HR 0.24, 95% CI 0.07-0.80, P = 0.02), whereas patients who had female donors (HR 3.03, 95% CI 1.11-8.25, P = 0.03), donors ≥40-years (HR 6.95, 95% CI 1.93-25.03, P = 0.003), and those who received tacrolimus (HR 4.39, 95% CI 1.47-13.13, P = 0.008) and mycophenolate mofetil (HR 6.37, 95% CI 1.62-25.13, P = 0.008) had higher risk. Survival was similar in patients with de novo AIH compared with the LT population (mean survival time, 17 ± 1.5 vs. 16 ± 0.5 years, Log-rank test; P = 0.4). CONCLUSIONS: The incidence of de novo AIH is low and does not impact on long-term survival. Recipients of female or older donor grafts, or recipients using tacrolimus, or mycophenolate mofetil as part of their immunosuppressive regimen have a higher risk of de novo AIH, whereas LT recipients maintained on cyclosporine A have a lower risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".