{"id":"W4382182335","doi":"10.1148/radiol.222855","title":"A Multicenter Assessment of Interreader Reliability of LI-RADS Version 2018 for MRI and CT","year":2023,"lang":"en","type":"article","venue":"Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; Radiological Society of North America","keywords":"Medicine; Intraclass correlation; Malignancy; Radiology; Nuclear medicine; Multicenter study; Surgery; Internal medicine","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":[0.0005401495,0.00006652171,0.0003189438,0.0000907536,0.00001938181,0.000001337175,0.00005108202,0.00004343754,0.00002772808],"category_scores_gemma":[0.0004776643,0.00005130525,0.00006619023,0.00007114285,0.0002130075,0.00001860582,0.00004405985,0.0001422409,0.000001733295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002884872,"about_ca_system_score_gemma":0.00003191775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003929067,"about_ca_topic_score_gemma":5.82892e-7,"domain_scores_codex":[0.9993173,0.00005776193,0.0002346308,0.0001738048,0.0000732613,0.0001432337],"domain_scores_gemma":[0.9993639,0.0002849228,0.00008297787,0.0001622186,0.00004534232,0.00006061228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004321994,0.0002268701,0.9179677,0.001233369,0.0001753409,0.00002620772,0.0007672441,0.0002401196,0.03433622,0.0005348526,0.0328755,0.01118432],"study_design_scores_gemma":[0.004825745,0.001190684,0.620294,0.0002580631,0.0001587488,0.0001059885,0.0002929156,0.348669,0.000974939,0.0003194661,0.02280095,0.0001095213],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894191,0.00009074157,0.005895431,0.003704251,0.0003275965,0.0002646517,0.00000838751,0.0000237023,0.0002661459],"genre_scores_gemma":[0.9919743,0.0001439761,0.007412669,0.0001855149,0.00006445697,0.00001026392,0.00003105235,0.000009643233,0.0001681361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3484288,"threshold_uncertainty_score":0.2092168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01511900139982711,"score_gpt":0.3520147720061128,"score_spread":0.3368957706062857,"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."}}