Simultaneous Liver Kidney Transplantation: A Medical Decision Analysis
Bibliographic record
Abstract
BACKGROUND: The use of simultaneous liver kidney transplantation has increased dramatically. When the liver and kidney are available from the same deceased donor, what is the best decision? There are two allocation options. In the combined allocation, both organs are allocated to a liver failure (end-stage liver disease [ESLD]) patient on dialysis leaving an end-stage renal disease (ESRD) patient on dialysis. In split allocation, the liver is allocated to the liver failure patient on dialysis and the kidney to the patient with ESRD. METHODS: A computerized medical decision analysis was performed using published US survival data. The two options were compared by examining differences in projected quality-adjusted life years (QALYs). RESULTS: Combined allocation was the best strategy (+0.806 QALYs) if liver transplant recipients on dialysis have proportionately worse survival compared with kidney failure alone patients on dialysis. However, because some patients with hepatorenal syndrome recover kidney function post-liver transplant alone (LTA), a second analysis incorporated the possibilities of being dialysis free. If the chance of recovery of renal function is 50% rather than 0%, the decision reversed. Here, the split allocation provided 1.02 more total QALYs than the combined allocation. CONCLUSIONS: This study demonstrates that simultaneous liver kidney transplantation is an excellent strategy in most patients with both ESLD and ESRD. However, allocating a kidney to a patient with ESLD, who has the potential to be dialysis free without a kidney transplant, does not maximize overall outcomes when all patients are considered.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".