{"id":"W3199509600","doi":"10.1002/pros.24233","title":"Predicting survival after radical prostatectomy: Variation of machine learning performance by race","year":2021,"lang":"en","type":"article","venue":"The Prostate","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Prostatectomy; Medicine; Race (biology); Confidence interval; Prostate cancer; Classifier (UML); African american; Artificial intelligence; Cancer; Internal medicine; Computer science; Biology","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":[0.0003974399,0.0001569418,0.000289527,0.00002843146,0.0001201873,0.0000246309,0.00005517308,0.00004461261,0.0001290815],"category_scores_gemma":[0.0001089737,0.0001041424,0.00007046854,0.0002280758,0.00009115124,0.000100555,0.00007439586,0.0002917494,0.00001419507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009602861,"about_ca_system_score_gemma":0.0001617027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008341209,"about_ca_topic_score_gemma":0.000009703966,"domain_scores_codex":[0.9987115,0.0001231982,0.0003122132,0.000260949,0.0003152058,0.0002769217],"domain_scores_gemma":[0.9992854,0.0001124287,0.0001106668,0.0002493659,0.0001567354,0.00008542639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001682365,0.0005016629,0.9544361,0.0005358973,0.0003431606,0.00009582812,0.006173469,0.0002168792,0.01939635,0.00004819544,0.0001244836,0.01644564],"study_design_scores_gemma":[0.007570383,0.001566391,0.7162889,0.0007992314,0.0005382301,0.0001227906,0.0003364053,0.01269603,0.2459018,0.0001330696,0.01369438,0.0003524424],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927585,0.003638647,0.00009031242,0.002220945,0.0001362609,0.0005845713,0.0000867776,0.00005984227,0.0004241954],"genre_scores_gemma":[0.9954892,0.002320222,0.0002334786,0.0001555177,0.00005773599,0.0001788502,0.0001552273,0.00003028203,0.001379528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2381472,"threshold_uncertainty_score":0.4246804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0083149551864527,"score_gpt":0.2373919630319051,"score_spread":0.2290770078454524,"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."}}