Living vs. Deceased Donor Liver Transplantation Provides Comparable Recovery of Renal Function in Patients With Hepatorenal Syndrome: A Matched Case–Control Study
Bibliographic record
Abstract
Outcomes of living versus deceased donor liver transplantation in patients with chronic liver disease and hepatorenal syndrome (HRS) was compared using a matched pair study design. Thirty patients with HRS receiving a live donor liver transplantation (LDLT) and 90 HRS patients receiving a full graft deceased donor liver transplantation (DDLT) were compared. LDLT versus DDLT of patients with HRS was associated with decreased peak aspartate aminotransferase levels (339 ± 214 vs. 935 ± 1253 U/L; p = 0.0001), and similar 7-day bilirubin (8.42 ± 7.89 vs. 6.95 ± 7.13 mg/dL; p = 0.35), and international normalized ratio levels (1.93 ± 0.62 vs. 1.78 ± 0.78; p = 0.314). LDLT vs. DDLT had a decreased intensive care unit (2 [1-39] vs. 4 [0-93] days; p = 0.004), and hospital stay (17 [4-313] vs. 26 [0-126] days; p = 0.016) and a similar incidence of overall postoperative complications (20% vs. 27%; p = 0.62). No difference was detected between LDLT and DDLT patients regarding graft survival at 1 (80% vs. 82%), at 3 (69% vs. 76%) and 5 years (65% vs. 76%) (p = 0.63), as well as patient survival at 1 (83% vs. 82%), 3 (72% vs. 77%) and 5 years (72% vs. 77%) (p = 0.93). The incidence of chronic kidney disease post-LT (10% vs. 6%; p = 0.4) was similar between both groups. LDLT results in identical long-term outcome when compared with DDLT in patients with HRS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".