Single-Center North American Experience of Liver Transplantation in Autoimmune Hepatitis: Infrequent Indication but Good Outcomes for Patients
Bibliographic record
Abstract
Abstract Background and Aims A 40% risk of disease recurrence post-liver transplantation (LT) for autoimmune hepatitis (AIH) has been previously reported. Risk factors for recurrence and its impact on long-term patient outcome are poorly defined. We aimed to assess prevalence, time to disease recurrence, as well as patient and graft survival in patients with recurrent AIH (rAIH) versus those without recurrence. Methods Single-center retrospective study of adult recipients who underwent LT for AIH between January 2007 and December 2017. Patients with AIH overlap syndromes were excluded. Results A total of 1436 LTs were performed during the study period, of whom 46 (3%) for AIH. Eight patients had AIH overlap syndromes and were excluded. Patients were followed up for 4.4 ± 3.4 years and mean age at LT was 46.8 years. Average transplant MELD (Model for End-Stage Liver Disease) score was 24.9. About 21% of patients (8 of 38) were transplanted for acute onset of AIH; 66% of patients (n = 25) received a deceased donor liver graft, and 34% a living donor organ. rAIH occurred in 7.8% (n = 3/38) of recipients. Time to recurrence was 1.6, 12.2 and 60.7 months. Patient and graft survival in patients without recurrence was 88.6% and 82.8% in 5 years, whereas in those with rAIH, it was 66.7%, respectively. Conclusion Although AIH recurs post-LT, our data indicate a lower recurrence rate when compared to the literature and excellent patient and graft survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".