Advances and innovations in living donor liver transplant techniques, matching and surgical training: Meeting report from the living donor liver transplant consensus conference
Bibliographic record
Abstract
The practice of LDLT currently delivers limited impact in western transplant centers. The American Society of Transplantation organized a virtual consensus conference in October 2021 to identify barriers and gaps to LDLT growth, and to provide evidence-based recommendations to foster safe expansion of LDLT in the United States. This article reports the findings and recommendations regarding innovations and advances in approaches to donor-recipient matching challenges, the technical aspects of the donor and recipient operations, and surgical training. Among these themes, the barriers deemed most influential/detrimental to LDLT expansion in the United States included: (1) prohibitive issues related to donor age, graft size, insufficient donor remnant, and ABO incompatibility; (2) lack of acknowledgment and awareness of the excellent outcomes and benefits of LDLT; (3) ambiguous messaging regarding LDLT to patients and hospital leadership; and (4) a limited number of proficient LDLT surgeons across the United States. Donor-recipient mismatching may be circumvented by way of liver paired exchange. The creation of a national registry to generate granular data on donor-recipient matching will guide the practice of liver paired exchange. The surgical challenges to LDLT are addressed herein and focuses on the development of robust training pathways resulting in proficiency in donor and recipient surgery. Utilizing strong mentorship/collaboration programs with novel training practices under the auspices of established training and certification bodies will add to the breadth and depth of training.
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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 | 0.044 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".