{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004673695,0.0006810845,0.0007599954,0.0009097119,0.0005000665,0.001723414,0.000965639,0.001196502,0.001470963],"category_scores_gemma":[0.0112998,0.0003336091,0.0005105535,0.0005345986,0.0003021585,0.00070662,0.0006419417,0.001046313,0.0005862237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008082318,"about_ca_system_score_gemma":0.002202731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004040633,"about_ca_topic_score_gemma":0.004058686,"domain_scores_codex":[0.9983217,0.0005724669,0.0002000038,0.0003488122,0.0004758367,0.0000811293],"domain_scores_gemma":[0.9935057,0.004157844,0.0004589084,0.0003373793,0.001396682,0.0001434479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001955917,0.000582213,0.01848159,0.0004888938,0.0003926863,0.0002610992,0.0002463151,0.1272565,0.04538863,0.003384817,0.008621308,0.79294],"study_design_scores_gemma":[0.0002164634,0.000625153,0.005367541,0.00007711956,0.0001609825,0.0002327018,0.00004382238,0.95034,0.034219,0.003881293,0.004796028,0.00004005743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1690711,0.00177709,0.8137432,0.001356219,0.0002600923,0.0004917609,0.001282839,0.009977737,0.002039943],"genre_scores_gemma":[0.4566091,0.0003440773,0.5396544,0.0006164206,0.0001023996,0.0002890702,0.0009830375,0.0001292501,0.001272263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004673695,"threshold_uncertainty_score":0.02471715,"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."}}