Differential Simultaneous Liver and Kidney Transplant Benefit Based on Severity of Liver Damage at the Time of Transplantation
Bibliographic record
Abstract
BACKGROUND: We evaluated the concept of whether liver failure patients with a superimposed kidney injury receiving a simultaneous liver and kidney transplant (SLKT) have similar outcomes compared to patients with liver failure without a kidney injury receiving a liver transplantation (LT) alone. METHODS: Using data from the United Network of Organ Sharing (UNOS) database, patients were divided into five groups based on pre-transplant model for end-stage liver disease (MELD) scores and categorized as not having (serum creatinine (sCr) ≤ 1.5 mg/dL) or having (sCr > 1.5 mg/dL) renal dysfunction. Of 30,958 patients undergoing LT, 14,679 (47.5%) had renal dysfunction, and of those, 5,084 (16.4%) had dialysis. RESULTS: Survival in those (liver failure with renal dysfunction) receiving SLKT was significantly worse (P < 0.001) as compared to those with sCr < 1.5 mg/dL (liver failure only). The highest mortality rate observed was 21% in the 36+ MELD group with renal dysfunction with or without SLKT. In high MELD recipients (MELD > 30) with renal dysfunction, presence of renal dysfunction affects the outcome and SLKT does not improve survival. In low MELD recipients (16 - 20), presence of renal dysfunction at the time of transplantation does affect post-transplant survival, but survival is improved with SLKT. CONCLUSIONS: SLKT improved 1-year survival only in low MELD (16 - 20) recipients but not in other groups. Performance of SLKT should be limited to patients where a benefit in survival and post-transplant outcomes can be demonstrated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".