First-Degree Living-Related Donor Liver Transplantation in Autoimmune Liver Diseases
Bibliographic record
Abstract
Liver transplantation (LT) is the treatment of choice for end-stage autoimmune liver diseases. However, the underlying disease may recur in the graft in some 20% of cases. The aim of this study is to determine whether LT using living donor grafts from first-degree relatives results in higher rates of recurrence than grafts from more distant/unrelated donors. Two hundred sixty-three patients, who underwent a first LT in the Toronto liver transplant program between January 2000 and March 2015 for autoimmune liver diseases, and had at least 6 months of post-LT follow-up, were included in this study. Of these, 72 (27%) received a graft from a first-degree living-related donor, 56 (21%) from a distant/unrelated living donor, and 135 (51%) from a deceased donor for primary sclerosing cholangitis (PSC) (n = 138, 52%), primary biliary cholangitis (PBC) (n = 69, 26%), autoimmune hepatitis (AIH) (n = 44, 17%), and overlap syndromes (n = 12, 5%). Recurrence occurred in 52 (20%) patients. Recurrence rates for each autoimmune liver disease were not significantly different after first-degree living-related, living-unrelated, or deceased-donor LT. Similarly, time to recurrence, recurrence-related graft failure, graft survival, and patient survival were not significantly different between groups. In conclusion, first-degree living-related donor LT for PSC, PBC, or AIH is not associated with an increased risk of disease recurrence.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".