{"id":"W3189544101","doi":"10.1097/ju.0000000000002064.17","title":"MP43-17 DIFFERENCES IN GLEASON SCORE (GS) DISTRIBUTION AND TUMOR AGGRESSIVENESS IN LARGE COHORTS OF ASIAN AND CAUCASIAN MEN","year":2021,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000559855,0.000180224,0.0003007783,0.0009075952,0.0004485736,0.0006573579,0.0002269743,0.0003598015,0.004883496],"category_scores_gemma":[0.002012175,0.000380818,0.0004465266,0.001111514,0.0002211739,0.0004187035,0.0006455409,0.0005276504,0.001030615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001600124,"about_ca_system_score_gemma":0.0003325868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005174052,"about_ca_topic_score_gemma":0.007478981,"domain_scores_codex":[0.9997464,0.00004538413,0.00002167216,0.00006540234,0.00007728793,0.0000438434],"domain_scores_gemma":[0.9991807,0.0001222971,0.0002696697,0.0001020463,0.0001197294,0.0002055142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004309622,0.00002581536,0.9838039,0.00003143568,0.0002129445,0.0001615514,0.0001619261,0.00004449083,0.0006103554,0.00009097708,0.004665987,0.009759494],"study_design_scores_gemma":[0.00001786687,0.00005642672,0.996496,0.00001873189,0.0000716287,0.000505659,0.0002222665,0.0001103092,0.0001002487,0.00008276886,0.002309313,0.000008824217],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894893,0.002625739,0.0002356577,0.0005844635,0.00009190109,0.00003151138,0.003834603,0.00003210422,0.003074757],"genre_scores_gemma":[0.9913077,0.001825377,0.0001879084,0.0002248701,0.0001546099,0.00004434086,0.002597803,0.00002676815,0.003630805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005174052,"threshold_uncertainty_score":0.01633692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008484821881420634,"score_gpt":0.2676586203371973,"score_spread":0.2591737984557767,"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."}}