Immunologic benefits of maternal living donor allografts in pediatric liver transplantation: fewer rejection episodes and no evidence of de novo allosensitization
Bibliographic record
Abstract
BACKGROUND: Pediatric liver transplant (LT) recipients of maternal living liver donor (LLD) grafts have been reported to experience fewer rejection episodes. However, it is unclear whether this benefit translates to reduction in developing donor-specific antibody (DSA) among maternal-LLD recipients. The aim of this study was to compare immunologic outcomes among maternal-LLD, non-maternal-LLD, and deceased donor liver transplant (DDLT) recipients. METHODS: Children (≤18 years) who underwent LT between 1/1998 and 12/2019 at two high-volume LT centers in North America were evaluated. Patients were divided into three groups by type of graft received (maternal-LLD, non-maternal LLD, and DDLT). Clinical variables and outcomes were compared according to each graft type. RESULTS: A total of 450 pediatric primary LT were analyzed: 275 (61.1%) DDLT, 73 (16.2%) maternal-LLD, and 102 (22.6%) non-maternal-LLD. Children receiving LLD grafts were less likely to develop rejection when compared to the DDLT group (DDLT 46.9% vs. maternal-LLD 31.5% vs. non-maternal-LLD 28.4%, p = 0.001). There was no difference in rejection rates between maternal and non-maternal-LLD recipients. A higher percentage of maternal-LLD recipients were on immunosuppression monotherapy compared to non-maternal-LLD and DDLT recipients (6.7% vs. 1.2 vs. 2.4%, respectively). A subgroup of 68 patients were tested for DSA post-LT. Maternal-LLD recipients were less likely to develop de novo DSA (maternal-LLD 11.8% vs. non-maternal-LLD 19.3% vs. DDLT 43%, p = 0.018). None of the maternal-LLD recipients developed antibody-mediated rejection. CONCLUSIONS: These data support the concept of immunologic benefit of maternal-LLD in pediatric LT, with lower rates of rejection and allosensitization post-LT when compared to DDLT recipients.
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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".