{"id":"W4361228771","doi":"10.1016/j.clgc.2023.03.012","title":"Toward Precision Medicine: Development and Validation of A Machine Learning Based Decision Support System for Optimal Sequencing in Castration-Resistant Prostate Cancer","year":2023,"lang":"en","type":"article","venue":"Clinical Genitourinary Cancer","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation of Korea","keywords":"Medicine; Prostate cancer; Precision medicine; Decision support system; Oncology; Cancer; Internal medicine; Machine learning; Artificial intelligence; Pathology; Computer science","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":[0.001574548,0.0001928518,0.0005510698,0.0002993194,0.0001148216,0.00001359598,0.0000821313,0.0001347216,0.00005824362],"category_scores_gemma":[0.0003289625,0.0001428165,0.00008598224,0.0004695846,0.0001209146,0.00008969843,0.00006666996,0.0002739659,0.000003712457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006940553,"about_ca_system_score_gemma":0.00169776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002439438,"about_ca_topic_score_gemma":0.00006587392,"domain_scores_codex":[0.9975615,0.0001148683,0.001017392,0.0004804698,0.0004771919,0.0003485366],"domain_scores_gemma":[0.9983718,0.0007451968,0.0002341249,0.0001609829,0.000293473,0.0001943847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03765785,0.0003781216,0.4483916,0.005995139,0.0004451167,0.001250425,0.005491612,0.03298217,0.04826891,0.00001180256,0.001890291,0.417237],"study_design_scores_gemma":[0.07491468,0.01654087,0.4153066,0.01987845,0.0009008613,0.00008667691,0.007994276,0.2073337,0.2305264,0.00009790494,0.02490908,0.001510474],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954417,0.001546513,0.0003015238,0.0007783187,0.0003478423,0.00140273,0.00006056654,0.00006361304,0.0000571811],"genre_scores_gemma":[0.9923803,0.00208753,0.003578536,0.00003681993,0.0002320265,0.0006668573,0.0002503127,0.00003533989,0.0007322503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4157265,"threshold_uncertainty_score":0.5823887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1484363886325666,"score_gpt":0.4358126648759478,"score_spread":0.2873762762433811,"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."}}