{"id":"W4409556119","doi":"10.1111/ctr.70148","title":"Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data","year":2025,"lang":"en","type":"article","venue":"Clinical Transplantation","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Toronto General Hospital; University of Toronto; University Health Network","funders":"Mitacs","keywords":"Medicine; Machine learning; Hyperparameter optimization; Support vector machine; Artificial intelligence; Artificial neural network; Hyperparameter; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001425876,0.0001841133,0.0004343724,0.0001051638,0.0001590617,0.00005196686,0.0001467519,0.0001351556,0.00005020785],"category_scores_gemma":[0.000329764,0.0001667757,0.0001344683,0.0002026019,0.0001292855,0.0001706346,0.00007621227,0.0004632633,0.0000190907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001391715,"about_ca_system_score_gemma":0.00006021855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000120567,"about_ca_topic_score_gemma":0.00001790868,"domain_scores_codex":[0.9977614,0.0003204848,0.0007881505,0.000708282,0.0001753315,0.0002463517],"domain_scores_gemma":[0.9982871,0.0009937356,0.00007530575,0.0003545798,0.0000510644,0.000238174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004374696,0.0001378318,0.8842805,0.0001699753,0.0001324776,0.00009506682,0.0001311523,5.797939e-7,0.0002764637,0.00001159356,0.001118373,0.1132085],"study_design_scores_gemma":[0.005995158,0.0005279253,0.8677409,0.0009331678,0.001982069,0.00004818687,0.0001273031,0.08557078,0.0006801622,0.0000403834,0.03604291,0.0003110278],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741054,0.003680866,0.01665562,0.002997613,0.0003264135,0.0007378068,0.0001815864,0.0001814177,0.001133323],"genre_scores_gemma":[0.9845424,0.005371912,0.006334515,0.001307209,0.0001214592,0.00001589644,0.001971851,0.00002117123,0.0003135793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1128975,"threshold_uncertainty_score":0.6800914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1509773587225985,"score_gpt":0.3754773212475327,"score_spread":0.2244999625249342,"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."}}