{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004687686,0.0002358829,0.0003169469,0.0006532139,0.0002435392,0.0006060004,0.0003059288,0.0003325069,0.0005750067],"category_scores_gemma":[0.01016837,0.00009509672,0.0004696876,0.0004831828,0.0003823828,0.0003605742,0.0004213276,0.0004526298,0.0002526618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003577999,"about_ca_system_score_gemma":0.0003075087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002839732,"about_ca_topic_score_gemma":0.002387609,"domain_scores_codex":[0.998395,0.0007016373,0.0001251122,0.0004086731,0.0002343572,0.0001352472],"domain_scores_gemma":[0.9931879,0.003555558,0.001380829,0.0008932409,0.0007445102,0.0002379659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005417048,0.00007123359,0.9728739,0.00001793286,0.0002173125,0.00004705869,0.0001054105,0.007369261,0.00221219,0.0001179359,0.0006594269,0.01576667],"study_design_scores_gemma":[0.00002353772,0.0003375082,0.9301807,0.00002326347,0.0001286372,0.0003432783,0.0001504111,0.06314138,0.003504781,0.0008328743,0.001307771,0.00002591212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959458,0.0002229373,0.002812922,0.0001235206,0.00001545941,0.00001162306,0.0004466695,0.00003456784,0.0003865963],"genre_scores_gemma":[0.9983882,0.00003709753,0.0008499126,0.00003449336,0.00001292039,0.000007627,0.0006032867,0.000007783699,0.00005870054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004687686,"threshold_uncertainty_score":0.02479112,"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."}}