{"id":"W4310370526","doi":"10.1148/rg.220066","title":"Universal Liver Imaging Lexicon: Imaging Atlas for Research and Clinical Practice","year":2022,"lang":"en","type":"article","venue":"Radiographics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Toronto","funders":"Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Medicine; Atlas (anatomy); Lexicon; Medical physics; Clinical Practice; Clinical imaging; Radiology; Artificial intelligence; Family medicine; Anatomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005626636,0.0001509008,0.0003123545,0.0005990789,0.0008929305,0.00008571516,0.0002052402,0.00004402232,0.00005042198],"category_scores_gemma":[0.001998626,0.0001543699,0.0002006073,0.0008062239,0.0007238538,0.0002032797,0.0002791275,0.001926906,0.000002222177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000105004,"about_ca_system_score_gemma":0.0002523208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000181574,"about_ca_topic_score_gemma":0.000002311245,"domain_scores_codex":[0.9974185,0.0004149501,0.0003778989,0.0005409916,0.0007077264,0.0005399589],"domain_scores_gemma":[0.9964657,0.002410888,0.0001217516,0.000340643,0.0003160289,0.000344948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001616931,0.001126661,0.6250417,0.0002574992,0.0005400007,0.002564696,0.001382432,0.00004437428,0.0004889433,0.05222594,0.1061859,0.2085249],"study_design_scores_gemma":[0.003731807,0.0005972337,0.01328892,0.00005899544,0.0002940827,0.002096062,0.002967702,0.1353425,0.00000762317,0.001761272,0.8396025,0.0002513143],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5963043,0.0418428,0.04348363,0.2732898,0.004645649,0.005009706,0.00009739355,0.0008901713,0.03443656],"genre_scores_gemma":[0.9571735,0.002921774,0.03276517,0.005055259,0.001035904,0.00006246799,0.00007142885,0.0001013466,0.0008131162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7334165,"threshold_uncertainty_score":0.8371557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05836767890696545,"score_gpt":0.4268881700065081,"score_spread":0.3685204910995427,"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."}}