{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003049085,0.0008397283,0.0005914171,0.003176505,0.0002134794,0.001089477,0.0004178325,0.0006399815,0.0007363387],"category_scores_gemma":[0.00511063,0.0001666954,0.0008421045,0.0009066819,0.0003143504,0.000818704,0.0005192573,0.0006568159,0.0003211155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003710018,"about_ca_system_score_gemma":0.0004672835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168071,"about_ca_topic_score_gemma":0.002432815,"domain_scores_codex":[0.9993081,0.0002674861,0.00008054852,0.0001431178,0.000139212,0.00006154562],"domain_scores_gemma":[0.9969427,0.002016651,0.000387004,0.0001671664,0.0003794192,0.0001070537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006805253,0.0004957705,0.5609398,0.0002731701,0.0009727046,0.0002660805,0.00008284831,0.1805559,0.005033888,0.0004085244,0.002106644,0.2481842],"study_design_scores_gemma":[0.00002530019,0.0004157601,0.07111259,0.00008046152,0.0001699714,0.0003337,0.00005210536,0.9225682,0.002304524,0.002053637,0.0008493321,0.00003437949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9266739,0.002500044,0.06577875,0.0007650401,0.00007495993,0.0001087412,0.00110722,0.0004869738,0.00250428],"genre_scores_gemma":[0.9877672,0.0002436087,0.01081262,0.00006431545,0.00004605474,0.00002507513,0.0008196948,0.0000116404,0.000209799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003176505,"threshold_uncertainty_score":0.01612532,"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."}}