{"id":"W2091928694","doi":"10.1016/j.acra.2015.03.010","title":"Validation of a Semiautomated Liver Segmentation Method Using CT for Accurate Volumetry","year":2015,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal; McGill University; Hôpital Saint-Luc; Montreal General Hospital","funders":"","keywords":"Repeatability; Medicine; Segmentation; Intraclass correlation; Nuclear medicine; Hepatocellular carcinoma; Radiology; Artificial intelligence; Mathematics; Computer science; Internal medicine","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":[0.0006735165,0.000114268,0.0003329607,0.0002966762,0.00002718903,0.000002351316,0.00005811923,0.0001860553,0.00002155864],"category_scores_gemma":[0.000262189,0.0001000601,0.00008313167,0.0003430828,0.00004122507,0.00009780181,0.00001670674,0.0001431386,0.000008261859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117665,"about_ca_system_score_gemma":0.0001164072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009058594,"about_ca_topic_score_gemma":2.611775e-7,"domain_scores_codex":[0.9989805,0.0001980181,0.0003328044,0.0002019814,0.0001017589,0.0001849701],"domain_scores_gemma":[0.9992558,0.0002030718,0.0001979157,0.0001177823,0.0001273504,0.00009804357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004372825,0.00005243361,0.623515,0.0001559954,0.00032818,0.00003600483,0.001054193,0.0001183456,0.3575721,0.000151035,0.003927647,0.01265175],"study_design_scores_gemma":[0.00449193,0.0007237585,0.004490935,0.00006446696,0.0009256224,0.0009342323,0.0002897015,0.2452335,0.7403269,0.0004063777,0.001921241,0.0001912764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687243,0.001208622,0.02886399,0.0001986404,0.0001427371,0.0007130197,0.00001394691,0.00005203156,0.00008271864],"genre_scores_gemma":[0.9424602,0.0001428307,0.05672771,0.000139345,0.0001339201,0.00004877912,0.0002320593,0.00001812223,0.0000970363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6190241,"threshold_uncertainty_score":0.4080334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.20814046061971,"score_gpt":0.3856816091583677,"score_spread":0.1775411485386577,"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."}}