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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".