{"id":"W4387296066","doi":"10.1148/radiol.230659","title":"Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis","year":2023,"lang":"en","type":"article","venue":"Radiology","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec","keywords":"Medicine; Steatosis; Grading (engineering); Nonalcoholic fatty liver disease; Receiver operating characteristic; Liver biopsy; Radiology; Biopsy; Fatty liver; Retrospective cohort study; Internal medicine; Disease","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":[0.000125127,0.00008083374,0.0005212755,0.0001053068,0.00003533831,0.000001314225,0.00003228501,0.00006414944,0.00001141536],"category_scores_gemma":[0.0001507321,0.00006471503,0.00008518584,0.00008759621,0.0001117947,0.00001293863,0.00001922165,0.0000496033,0.000002466549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002401481,"about_ca_system_score_gemma":0.00001656116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000316078,"about_ca_topic_score_gemma":0.000003678388,"domain_scores_codex":[0.9993421,0.00006334892,0.0002327584,0.0001556408,0.00005056049,0.0001556294],"domain_scores_gemma":[0.9991384,0.0005550444,0.0001120894,0.00009980446,0.00003314046,0.00006147908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000112729,0.0001075937,0.9896298,0.0002496146,0.0002541549,0.000007943787,0.0004007528,0.0001141823,0.001707211,0.0004082057,0.0001924953,0.006815289],"study_design_scores_gemma":[0.001718391,0.00143725,0.982348,0.00006268275,0.0003674307,0.00002111806,0.0002929775,0.007408559,0.005835073,0.0001480851,0.0002998261,0.00006066255],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967192,0.002547084,0.000106836,0.0001311213,0.0000645846,0.0003129516,0.000007838854,0.00002962433,0.00008077772],"genre_scores_gemma":[0.9987368,0.0007069957,0.0003370021,0.00002179977,0.00003068299,0.0000585827,0.0000720188,0.000009505864,0.00002660234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007294376,"threshold_uncertainty_score":0.2639003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04256214024268722,"score_gpt":0.3482886769457562,"score_spread":0.3057265367030689,"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."}}