{"id":"W4385441689","doi":"10.1148/radiol.223308","title":"Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules","year":2023,"lang":"en","type":"article","venue":"Radiology","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Malignancy; Radiology; Estimation; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001512171,0.0001065165,0.0003044588,0.0001169187,0.00008003339,0.000003113288,0.00004405366,0.00005809986,0.00001981236],"category_scores_gemma":[0.0003129979,0.00008638873,0.0001050034,0.0001561685,0.0000507233,0.00002921847,0.00001975533,0.00008878296,0.00001446641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005436743,"about_ca_system_score_gemma":0.00002279102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007502099,"about_ca_topic_score_gemma":0.0000081507,"domain_scores_codex":[0.9992091,0.00005386356,0.0002193926,0.0002359325,0.00006840953,0.0002132737],"domain_scores_gemma":[0.9992682,0.0003264123,0.0001491712,0.0001564304,0.0000512029,0.00004854302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004599238,0.0001641083,0.1962658,0.0004117758,0.0004203871,0.00009329263,0.000246172,0.004367176,0.007815798,0.0001803086,0.0004343797,0.7891409],"study_design_scores_gemma":[0.002002059,0.001531782,0.884169,0.0001494986,0.0003517559,0.0001454981,0.00007835174,0.1028037,0.00771815,0.0003423307,0.0005932587,0.000114549],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938657,0.00120811,0.003657012,0.0003121119,0.0001321892,0.0005846249,0.00001442871,0.0001366922,0.00008915235],"genre_scores_gemma":[0.9933425,0.0006215898,0.005278183,0.00002129789,0.000101665,0.0002188333,0.0001817922,0.00002293622,0.0002112219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7890263,"threshold_uncertainty_score":0.352283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281289072910267,"score_gpt":0.282955601531288,"score_spread":0.2701427108021853,"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."}}