{"id":"W4385235870","doi":"10.1200/cci.23.00057","title":"Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer","year":2023,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Prostate cancer; Oncology; Enzalutamide; Logistic regression; Internal medicine; Medicine; Androgen deprivation therapy; Cohort; Cancer; Machine learning; Artificial intelligence; Computer science; Androgen receptor","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":[0.001270349,0.0002308164,0.0007508465,0.0003644312,0.00008470671,0.00003273432,0.00010049,0.000106906,0.00003249765],"category_scores_gemma":[0.0003898905,0.0001766948,0.0001271405,0.0006339798,0.0002083196,0.0002407822,0.00008802934,0.0006320284,0.000005557597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000289946,"about_ca_system_score_gemma":0.0009938398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001784383,"about_ca_topic_score_gemma":0.0007526953,"domain_scores_codex":[0.9974435,0.000148936,0.001428092,0.0002044459,0.0003493048,0.0004257175],"domain_scores_gemma":[0.9985178,0.0003635758,0.000567638,0.0001785672,0.0002143966,0.0001580985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002266006,0.0007165995,0.8666712,0.002317769,0.0006708071,0.00004599904,0.005676787,0.09328318,0.0142256,0.00001776075,0.0001146634,0.0139936],"study_design_scores_gemma":[0.01068596,0.0003856591,0.04050282,0.000918654,0.0003654214,0.000002589167,0.0005986642,0.9438032,0.002464571,0.00004964954,0.00004382883,0.0001789684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964809,0.001384329,0.0001253865,0.0002850193,0.0001546389,0.001255462,0.0002174707,0.00007405661,0.0000227163],"genre_scores_gemma":[0.9901359,0.005110891,0.00364568,0.0002619856,0.00005027793,0.0002128822,0.0002418905,0.00004215146,0.0002983583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.85052,"threshold_uncertainty_score":0.7205403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1937656149995228,"score_gpt":0.4384379232149948,"score_spread":0.244672308215472,"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."}}