ArticleVirology journal2024
A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data.
Article in Virology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Association of week-12 HBsAg Decline with HBsAg Clearance in Patients with Hepatitis B-Related Cirrhosis Receiving Pegylated Interferon Combined with Nucleos(T)ide Analogue Therapy: A Retrospective Cohort Study.International journal of general medicine · 2026Article
- [Research progress on predictive indicators of a clinical cure for chronic hepatitis B].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- A machine learning-based predictive model for 48-week hepatitis B surface antigen seroclearance in chronic hepatitis B patients treated with pegylated interferon α-2b: prediction at week 24.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundA multivariate predictive model was constructed using baseline and 12-week clinical data to evaluate the rate of clearance of hepatitis B surface antigen (HBsAg) at the 48-week mark in patients diagnosed with chronic hepatitis B who are receiving treatment with pegylated interferon α (PEG-INFα).
methodsThe study cohort comprised CHB patients who received pegylated interferon treatment at Mengchao Hepatobiliary Hospital, Fujian Medical University, between January 2019 and April 2024. Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis. Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. The efficacy of these models was assessed through various performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score.
resultsThis study included a total of 224 individuals diagnosed with chronic hepatitis B. The variables baseline log
conclusionsThe combined use of the baseline log
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