Prediction of Early Recurrence of Solitary Hepatocellular Carcinoma after Orthotopic Liver Transplantation
Bibliographic record
Abstract
Hepatocellular carcinomas(HCC) consisted of heterogeneous subtypes with different recurrence probabilities after liver transplantation(LT). Our study aimed to develop an improved model for predicting the recurrence of solitary HCC after LT. In this retrospective study, 151 solitary HCC patients who received orthotopic LT over a period of 10 consecutive years were included. All recipients received graft from deceased donors. The first eligible 50 patients were used as validation cohort and others were utilized to construct the model. A two-tailed P < 0.05 was considered to indicate statistical significance for all analysis. Based on the maximisation of the Youden's index, the optimal cutoff values for alpha-fetoprotein(AFP) and tumor diameter were 261.6 ng/mL and 3.6 cm, respectively. Vascular involvement includes gross and microscopic vascular invasion. Variables potentially affecting recurrence-free survival(RFS) were examined using univariate and multivariate Cox regression analysis. Univariate and multivariate analysis revealed that AFP, tumor diameter, vascular invasion and cytokeratin-19/glypican-3 sub-typing were independent prognostic factors for RFS, thus comprised the risk scoring model. The AUC values of the model in the cohorts were significantly higher than that of the Milan, UCSF, Fudan and Hangzhou criteria. These findings suggest the model has high performance in predicting early recurrence of solitary HCC patients after LT.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".