Hepatitis<scp>B</scp>virus mutation may play a role in hepatocellular carcinoma recurrence: A systematic review and meta‐regression analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: A number of studies have confirmed that antiviral therapy with nucleotide analogs (NAs) can improve the prognosis of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after curative therapy. However, what factors affected the prognosis of HBV-HCC after removal of the primary tumor and inhibition of HBV replication? A meta-regression analysis was conducted to explore the prognostic factor for this subgroup of patients. METHODS: MEDLINE, EMBASE, Web of Science, and Cochrane library were searched from January 1995 to February 2014 for clinical trials evaluating the effect of NAs on the prognosis of HBV-HCC after curative therapy. Data were extracted for host, viral, and intervention information. Single-arm meta-analysis was performed to assess overall survival (OS) rates and HCC recurrence. Meta-regression analysis was carried out to explore risk factors for 1-year OS rate and HCC recurrence for HBV-HCC patients after curative therapy and antiviral therapy. RESULTS: Fourteen observational studies with 1284 patients met the inclusion criteria. Influential factors for prognosis of HCC were mainly baseline HBeAg positivity, cirrhotic stage, advanced Tumor-Node-Metastasis (TNM) stage, macrovascular invasion, and antiviral agent type. The 1-year OS rate decreased by more than four times (coefficient -4.45, P<0.001) and the 1-year HCC recurrence increased by more than one time (coefficient 1.20, P=0.003) when lamivudine was chosen for HCC after curative therapy, relative to entecavir for HCC. CONCLUSIONS: HBV mutation may play a role in HCC recurrence. Entecavir or tenofovir, a high genetic barrier to resistance, should be recommended for HBV-HCC patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".