Financial toxicity in living donor liver transplantation: A call to action for financial neutrality
Bibliographic record
Abstract
After 2 decades of limited growth, living donor liver transplant (LDLT) has been increasingly accepted as a promising solution to the growing organ shortage in the US. With experience, LDLT offers superior graft and patient survival with low rates of rejection. However, not all waitlisted patients have equal access to LDLT, with financial toxicity representing a substantial barrier. Potential living liver donors face indirect, direct, and opportunity costs associated with donation as well as insurance-based discrimination and variable employer leave policies. There are multiple potential national, local, and patient-centered solutions to address some of the cost-related issues associated with living LDLT. These include standardization of employer leave policies, creation of federal and state-led tax relief programs, optimization of National Living Donor Assistance Center use, engagement of independent living donor advocates, creation of financial toolkits, and encouragement of recipient or donor-led fundraising. In this piece, members of the North American Living Liver Donation Group, a consortium of 37 LDLT programs, explore these financial challenges and discuss solutions to achieve financial neutrality, where individuals can donate free from financial constraints or gains. As a community, it is imperative that we confront factors driving financial toxicity to improve equity and access to LDLT.
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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 | 0.073 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.100 | 0.106 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".