Risk Factors for Intrahepatic Recurrence after Resection of Hepatocellular Carcinomas in Patients with Hepatitis B Virus Infection
Bibliographic record
Abstract
PURPOSE: Although surgical resection offers patients with HCC the chance of a cure, the post-resection tumor recurrence rate is high, with reported cumulative 5-year tumor recurrence rates ranging from 40 to 70%. The objective of this study was to investigate risk factors for intrahepatic recurrence after resection of hepatocellular carcinoma, especially in patients with hepatitis B virus infection. METHODS: Between January 1999 and December 2003, 59 patients in our Hospital with hepatitis B virus infection underwent liver resection for hepatocellular carcinoma. Clinical, biological, and histopathological characteristics of these patients were collected and tested for their prognostic significance using a Chi-square test and a Student's t-test. Time to recurrence and survival rate were analyzed by the Kaplan-Meier method. RESULTS: Of the 59 patients who underwent liver resection, 24 (41%) experienced intrahepatic recurrence. The 1-, 3-, and 5-year overall survival rates of total enrolled patients were 83%, 63%, and 42%, respectively. The 1-, 3-, and 5-year overall survival rates after recurrence were 87%, 52%, and 20%, respectively. The risk factors for early recurrence were elevated serum aspartate aminotransferase (AST) level (p=0.044) and larger tumor size (p=0.049). For late recurrence, greater tumor size (p=0.039) was the only risk factor. CONCLUSION: Tumor size and serum aspartate aminotransferase are risk factors of intrahepatic recurrence after resection of HCC in patients with chronic hepatitis B virus infection. This finding indicates that patients who have these risk factors should be under more careful supervision and have more aggressive follow-up.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".