Practical Considerations of Right Lobe Living Donor Liver Transplantation in Adults
Bibliographic record
Abstract
The practice of living donor liver transplantation in adults has developed rapidly over the past five years and brings with it a set of unique technical and ethical challenges. The evaluation of potential donors focuses on their health and motives, and the results of noninvasive imaging, with the objective of ensuring the best outcomes for both donors and recipients. Graft volume is critical to success, and venous outflow reconstruction is paramount, although there is no consensus on the preferred method. Biliary tract complications occur in 30% of recipients. Complications that may interfere with recovery or delay the return to well-being occur in one of every four or five donors. The precise risk of donor death cannot be stated with certainty because comprehensive data on all cases are not available. It is clear, however, that donation of the right lobe of the liver carries with it a much greater risk of mortality than kidney donation. The paucity of details reported on donors who have died make it impossible to determine to what extent the deaths were preventable. The option of living donation is an invitation to expand the criteria for recipient selection to include, for example, patients with tumours that exceed traditional transplant guidelines. The risk-benefit ratios for donors become especially problematic when post-transplant recipient survival is below current standards.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".