The predictive value of the modified AFP model for liver transplantation outcomes in multinodular hepatocellular carcinoma patients
Bibliographic record
Abstract
BACKGROUND: There is a lack of studies focusing on the benefit of liver transplantation (LT) in hepatocellular carcinoma (HCC) patients with > 3 tumors. This study aims to establish a model to effectively predict overall survival in Chinese HCC patients with multiple tumors (> 3 tumors) who undergo LT. METHODS: This retrospective study included 434 HCC liver transplant recipients from the China Liver Transplant Registry. All HCC patients had more than 3 tumor nodules. Three selection criteria systems (i.e., AFP, Metroticket 2.0, and Up-to-7) were compared regarding the prediction of HCC recurrence. The modified AFP model was established by univariate and multivariate competing risk analyses. RESULTS: The AFP score 2 and the AFP score ≥ 3 groups had 5-year recurrence rates of 19.6% and 40.5% in our cohort. The prediction of HCC recurrence based on the AFP model was associated with a c-statistic of 0.606, which was superior to the Up-to-7 and Metroticket 2.0 models. AFP level > 1000 ng/mL, largest tumor size ≥ 8 cm, vascular invasion, and MELD score ≥ 15 were associated with overall survival. The 5-year survival rate in the modified AFP score 0 group was 71.7%. CONCLUSIONS: The AFP model is superior in predicting tumor recurrence in HCC patients with > 3 tumors prior to LT. With the modified AFP model, patients likely to derive sufficient benefit from LT can be identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".