Predictive Value of Alpha-Fetoprotein Change Rates for Hepatocellular Carcinoma Recurrence After Liver Transplant
Bibliographic record
Abstract
Background: Liver transplantation offers a conclusive solution for individuals diagnosed with Hepatocellular carcinoma (HCC). Alterations in alpha-fetoprotein (AFP) levels prior to transplantation could serve as an indicator for the potential recurrence of HCC. Objective: This study aimed to evaluate the capacity of variations in the pre-transplant AFP levels as a prognostic indicator for tumor recurrence after transplant. Patients and methods: 144 HCC patients had liver transplant over a 20-year period at our institute. Their mean age at time of transplant was 54.8 years, and 124 patients were males. 71 patients (49.3%) received interventions for HCC (group 1), while 73 patients (50.7%) had no HCC treatment (group 2). Results: The recurrence-free survival rate was 86.8%. HCC recurrence was reported in 19 patients (13.2%), including 17 males and 2 females. Among these 19 patients, 11 were in group 1 (15.5%), and 8 were in group 2 (11%). The predictors of recurrence were a tumor volume greater than 115 cc (P = 0.010), and AFP > 400 ng/ml (P = 0.009). While AFP gradient (AFP-G) did not exhibit a significant correlation with tumor recurrence in the entire cohort, it demonstrated a notable correlation with recurrence in untreated patients. A specific AFP-G threshold of 60 ng/ml/month was chosen. An AFP-G of 60 ng/ml/month displayed a sensitivity of 83.3% and a specificity of 94.7% for predicting HCC recurrence. Conclusion: It was noted that using an AFP-G exceeding 60 ng/ml/month should be regarded as a prognostic tool rather than a strict selection criterion for transplant candidates.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".