Liver transplantation for overlap syndromes of autoimmune liver diseases
Bibliographic record
Abstract
BACKGROUND/AIMS: The term overlap syndrome describes variant forms of autoimmune hepatitis (AIH) that present in combination with either characteristics of primary biliary cirrhosis (PBC), or primary sclerosing cholangitis (PSC). This study analysed the outcomes and evidence of recurrent liver disease after liver transplantation in patients with overlap syndromes compared with patients transplanted for single autoimmune liver disease. METHODS: We evaluated 231 adult patients who received a liver transplant as a result of autoimmune liver diseases; including 103 with PBC, 84 with PSC, 32 with AIH and 12 with overlap syndrome (7 AIH-PBC and 5 AIH-PSC). RESULTS: Patients with overlap syndromes had a higher probability of recurrence than patients with a single autoimmune liver disease (5 years: 53% vs. 17%; 10 years 69% vs. 29%, P = 0.001). Furthermore, median time for recurrence in overlap syndrome was shorter when compared with patients with single autoimmune liver disease (67 ± 20 vs. 172 ± 9 months, P = 0.001). The diagnosis of overlap syndrome was independently associated with a higher risk to develop recurrent disease than patients transplanted with a single disease (HR 3.39, P = 0.007). Median graft survival for overlap syndrome was 123 ± 16 months and 180 ± 8 months in patients with single autoimmune liver diseases (P = 0.9), and median patient survival for overlap syndrome was 135 ± 13 months and 193 ± 8 months in patients with single autoimmune liver disease (P = 0.6). CONCLUSIONS: Patients that received an allograft for end-stage liver disease secondary to overlap syndrome had a higher rate of disease recurrence when compared with transplant recipients with single autoimmune liver disorders, but the overall survival was comparable.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".