Resource Utilization of Living Donor Versus Deceased Donor Liver Transplantation Is Similar at an Experienced Transplant Center
Bibliographic record
Abstract
Although living donor liver transplantation (LDLT) has been shown to decrease waiting-list mortality, little is known of its financial impact relative to deceased donor liver transplantation (DDLT). We performed a retrospective cohort study of the comprehensive resource utilization, using financial charges as a surrogate measure-from the pretransplant through the posttransplant periods-of 489 adult liver transplants (LDLT n = 86; DDLT n = 403) between January 1, 2000, through December 31, 2006, at a single center with substantial experience in LDLT. Baseline characteristics differed between LDLT versus DDLT with regards to age at transplantation (p = 0.02), male gender (p < 0.01), percentage Caucasians (p < 0.01) and transplant model for end-stage liver disease (MELD) score (p < 0.01). In univariate analysis, there was a trend toward decreased total transplant charges with LDLT (p = 0.06), despite increased surgical charges associated with LDLT (p < 0.01). After adjustment for the covariates that were associated with financial charges, there was no significant difference in total transplant charges (p = 0.82). MELD score at transplant was the strongest driver of resource utilization. We conclude that at an experienced transplant center, LDLT imposes a similar overall financial burden than DDLT, despite the increased complexity of living donor surgery and the addition of the costs of the living donor. We speculate that LDLT optimizes transplantation by transplanting healthier and younger 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".