Living-Donor Liver Transplantation for Hepatocellular Carcinoma: Impact of the MELD Score and Predictive Value of NLR on Survival
Bibliographic record
Abstract
Background: Patients with hepatocellular carcinoma (HCC) tend to be referred for liver transplantation (LT) at an early stage of cirrhosis, with lower pre-LT Model of End-Stage Liver Disease (MELD) scores. We investigated the impact of high MELD scores on post-LT outcomes in patients with HCC and validated the prognostic significance of the neutrophil-to-lymphocyte ratio (NLR). Patients and Method: This retrospective single-center cohort study enrolled 230 patients with HCC who underwent LDLT from 2004−2019 in our institute. We defined a high MELD score as ≥20. Results: The MELD < 20 and MELD ≥ 20 groups comprised 205 and 25 cases, respectively. Although there was no significant difference in disease-free survival between the two groups (p = 0.629), the incidence of septic shock (p = 0.019) was significantly higher in the high MELD group. The one-, three-, and five-year overall survival rates were not significantly different between the two groups (p = 0.056). In univariate analysis, a high pre-LT NLR was associated with poorer survival in the high MELD group (p = 0.029, hazard ratio [HR]: 1.07, 90% confidence interval [CI]: 1.02−1.13). NLR cut-off values of ≥10.7 and <10.7 were predictive of mortality, with an AUC of 0.705 (90% CI: 0.532−0.879). The one-, three-, and five-year post-LT survival rates were significantly higher among the recipients with an NLR < 10.7 than those with an NLR ≥ 10.7 (p = 0.005). Conclusions: Pre-LT MELD score ≥ 20 was associated with a higher risk of developing post-LT septic shock and mortality. The pre-LT serum NLR is a useful predictive factor for clinical outcomes in patients with HCC with high MELD scores.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".