{"id":"W3202919466","doi":"10.1016/j.jhep.2021.09.025","title":"An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B","year":2021,"lang":"en","type":"article","venue":"Journal of Hepatology","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":145,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network","funders":"Ministry of Science and ICT, South Korea; National IT Industry Promotion Agency","keywords":"Entecavir; Medicine; Hepatocellular carcinoma; Internal medicine; Cohort; Cirrhosis; Hepatitis B; Chronic hepatitis; Gastroenterology; Framingham Risk Score; Risk model; Oncology; Immunology; Disease","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007104063,0.0004061679,0.0004143872,0.0007718627,0.0003318157,0.0009053473,0.0003584063,0.0003323808,0.001262014],"category_scores_gemma":[0.001981631,0.000127171,0.00076111,0.0004636467,0.0001287794,0.0002809332,0.0002777732,0.0005246819,0.0001756901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005170713,"about_ca_system_score_gemma":0.0007139539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01351148,"about_ca_topic_score_gemma":0.009166162,"domain_scores_codex":[0.9998585,0.00004872474,0.00001775277,0.00002767898,0.00001788126,0.00002948428],"domain_scores_gemma":[0.9994594,0.0003288989,0.00005114406,0.00002344433,0.00008984729,0.00004731063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001490803,0.0010818,0.8952165,0.00006219876,0.0008707334,0.0005632156,0.0001645139,0.06281102,0.00101653,0.0004653467,0.001830046,0.03442735],"study_design_scores_gemma":[0.00007551774,0.0005176916,0.1529603,0.00002826438,0.0005663361,0.0002838861,0.0003510277,0.8436691,0.0004121764,0.0005729611,0.0005394959,0.0000232899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952328,0.0001930873,0.002771421,0.0003165193,0.00003856178,0.00003304938,0.0004062146,0.00002772362,0.0009805849],"genre_scores_gemma":[0.9982548,0.00007570841,0.0008402817,0.00003985746,0.00001054974,0.00001994543,0.0003885521,0.000001760877,0.0003684369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01351148,"threshold_uncertainty_score":0.02686566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03269028475314607,"score_gpt":0.2535017724046802,"score_spread":0.2208114876515341,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}