Liver Transplantation for Hepatocellular Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To analyze patient and tumor characteristics that influence patient survival to select patients who would most benefit from liver transplantation. SUMMARY BACKGROUND DATA: The selection of patients with hepatocellular carcinoma (HCC) for liver transplantation remains controversial. METHODS: One hundred twelve patients with nonfibrolamellar HCC who underwent a liver transplant from 1985 to 2000 were reviewed. Survival was calculated using the Kaplan-Meier method, with differences in outcome assessed using the log-rank procedure. Multivariate analysis was then performed using a Cox regression model. RESULTS: Overall patient survival rates were 78%, 63%, and 57% at 1, 3, and 5 years, respectively. Patients infected with the hepatitis B virus had a worse 5-year survival than those who were not (43% vs. 64%), with most deaths being attributed to recurrent hepatitis B. However, patients with hepatitis B virus who underwent more recent transplants using antiviral therapy fared as well as those who were negative for the virus, showing a 5-year survival rate of 77%. Patients with vascular invasion by tumor had a worse 5-year survival than patients without vascular invasion (33% vs. 68%). Vascular invasion, tumor size greater than 5 cm, and poorly differentiated tumor grade were predictors of tumor recurrence by univariate analysis; however, only vascular invasion remained significant on multivariate analysis: the rate of tumor recurrence at 5 years was 65% in patients with vascular invasion and only 4% for patients without vascular invasion. CONCLUSIONS: For well-selected patients with HCC, liver transplantation in the current era can achieve equivalent results to transplantation for nonmalignant indications. Vascular invasion is an indicator of high risk of tumor recurrence but is difficult to detect before transplantation.
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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 | 0.000 | 0.001 |
| 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) | 0.006 | 0.001 |
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".