{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06448997,0.000625838,0.0007162491,0.005355805,0.000946552,0.001428017,0.001389779,0.0006563604,0.001602642],"category_scores_gemma":[0.08626428,0.0004859153,0.001205494,0.003100919,0.0009929924,0.001370102,0.001878239,0.0005945462,0.0007027441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008504761,"about_ca_system_score_gemma":0.001314624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158464,"about_ca_topic_score_gemma":0.001899379,"domain_scores_codex":[0.9506166,0.0235519,0.01117886,0.007143477,0.006905363,0.0006037431],"domain_scores_gemma":[0.8622382,0.04508361,0.03585627,0.01878897,0.03643803,0.001595021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001653012,0.000211694,0.9394182,0.0004737084,0.0006803519,0.00007326612,0.002222114,0.0005834585,0.004838763,0.0006560601,0.001850233,0.04733913],"study_design_scores_gemma":[0.0001776786,0.001839007,0.9764959,0.0002023645,0.0004901013,0.0006212346,0.001049221,0.00397236,0.006795437,0.0005472113,0.007725771,0.00008372603],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9370997,0.002759519,0.04458348,0.0002520588,0.000223762,0.00180168,0.003159796,0.0006543366,0.009465688],"genre_scores_gemma":[0.949126,0.0002325535,0.04518454,0.0001284089,0.0001046969,0.001903973,0.002166939,0.0001626618,0.0009902198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06448997,"threshold_uncertainty_score":0.3410596,"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."}}