Living donor financial assistance programs in liver transplantation: The global perspective
Bibliographic record
Abstract
Living donor liver transplantation (LDLT) has increased availability of liver transplantation, particularly in countries with limited access to deceased organ donors. It is unclear how individual countries address the financial impact of donation for potential living donors. Herein, living liver donor financial supports were examined, focusing on countries performing ≥10 LDLT per year in the World Health Organization Transplant Observatory. Categories included health insurance coverage, reimbursement of lost wages, employment protection, and other incentives designed to promote living liver donation. Overall, 26 countries have some form of asssistance in removing disincentives to ease the financial burden of living donation, ranging from childcare, accommodations, meals, and travel reimbursement, to coverage of medical complications post-donation. Most countries provide donation-related medical coverage. Fourteen provide reimbursement of lost wages and/or paid time off. Several unique programs were designed to incentivize living donation, including free entry to museums and observatories, parking and airline discounts, and exemptions on mortgages and medical deductibles. This study highlights the broad range of programs designed to support living liver donation in high-volume LDLT countries. The data collected in this study can provide a framework for other nations to propose and implement ethical reimbursement and incentivization for living liver donors.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".