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This paper presents HCC-STAR, a clinically aligned large language model for risk stratification and treatment guidance in hepatocellular carcinoma, aiming to improve precision therapy by leveraging electronic medical records.
This study evaluates machine learning models for pre-test risk stratification of Chlamydia trachomatis infection using non-invasive patient-reported data and urine biomarkers, demonstrating moderate predictive performance and the complementary value of both data types.