A comparison of survival and pathologic features of non-alcoholic steatohepatitis and hepatitis C virus patients with hepatocellular carcinoma
Bibliographic record
Abstract
AIM: To compare the clinical outcome and pathologic features of non-alcoholic steatohepatitis (NASH) patients with hepatocellular carcinoma (HCC) and hepatitic C virus (HCV) patients with HCC (another group in which HCC is commonly seen) undergoing liver transplantation. METHODS: Patients transplanted for HCV and NASH at our institution from January 2000 to April 2011 were analyzed. All explanted liver histology and pre-transplant liver biopsies were examined by two specialist liver histopathologists. Patient demographics, disease free survival, explant liver characteristics and HCC features (tumour number, cumulative tumour size, vascular invasion and differentiation) were compared between HCV and NASH liver transplant recipients. RESULTS: A total of 102 patients with NASH and 283 patients with HCV were transplanted. The incidence of HCC in NASH transplant recipients was 16.7% (17/102). The incidence of HCC in HCV transplant recipients was 22.6% (64/283). Patients with NASH-HCC were statistically older than HCV-HCC patients (P < 0.001). A significantly higher proportion of HCV-HCC patients had vascular invasion (23.4% vs 6.4%, P = 0.002) and poorly differentiated HCC (4.7% vs 0%, P < 0.001) compared to the NASH-HCC group. A trend of poorer recurrence free survival at 5 years was seen in HCV-HCC patients compared to NASH-HCC who underwent a Liver transplantation (P = 0.11). CONCLUSION: Patients transplanted for NASH-HCC appear to have less aggressive tumour features compared to those with HCV-HCC, which likely in part accounts for their improved recurrence free survival.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".