{"id":"W3014785343","doi":"10.1148/rycan.2020190079","title":"Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies","year":2020,"lang":"en","type":"article","venue":"Radiology Imaging Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; China Postdoctoral Science Foundation","keywords":"Malignancy; Feature (linguistics); Medical imaging; Cluster analysis; Medicine; Feature selection; Computer science; Pattern recognition (psychology); Artificial intelligence; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004683557,0.0001363393,0.0005660628,0.0001046334,0.00006711416,0.000009487548,0.0001351087,0.0000612525,0.00007672248],"category_scores_gemma":[0.001162032,0.000114227,0.00006785109,0.000143818,0.0003990496,0.00006458584,0.0000735572,0.0003040403,4.960159e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003433118,"about_ca_system_score_gemma":0.0001027862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003068855,"about_ca_topic_score_gemma":0.000005145792,"domain_scores_codex":[0.9986725,0.0000927938,0.0004417681,0.0002614932,0.0003233142,0.0002081712],"domain_scores_gemma":[0.9991655,0.000176355,0.0002409372,0.0001292695,0.00009959743,0.0001883484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004166719,0.00002075984,0.8725601,0.001720992,0.00009627981,0.00004670214,0.001469111,0.0000661806,0.1033185,0.001345303,0.003592433,0.015722],"study_design_scores_gemma":[0.00824621,0.0001671601,0.4139168,0.006206884,0.0006855108,0.002074772,0.002051851,0.5435603,0.01925316,0.0003051095,0.00291451,0.0006177232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301043,0.01780707,0.01651653,0.03408925,0.0003194943,0.0003499995,0.0000111309,0.00009613878,0.0007061039],"genre_scores_gemma":[0.9960199,0.0004445224,0.001314523,0.001911248,0.0002422803,0.0000112153,0.00001292877,0.00002370699,0.00001966428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5434941,"threshold_uncertainty_score":0.465804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360978325406095,"score_gpt":0.3196896325571045,"score_spread":0.3060798493030435,"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."}}