{"id":"W4411256986","doi":"10.1038/s41698-025-00959-w","title":"Utilizing cohort-level and individual networks to predict best response in patients with metastatic triple negative breast cancer","year":2025,"lang":"en","type":"article","venue":"npj Precision Oncology","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Cancer Institute; University of Texas MD Anderson Cancer Center; M.J. Murdock Charitable Trust; National Institutes of Health; Knight Cancer Institute, Oregon Health and Science University; Oregon Health and Science University; Breast Cancer Research Foundation; American Association for Cancer Research; AstraZeneca; Stand Up To Cancer; Prospect Creek Foundation; W. M. Keck Foundation","keywords":"Triple-negative breast cancer; Cohort; Medicine; Oncology; Breast cancer; Metastatic breast cancer; Cancer; Internal medicine; Triple negative","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":[0.001423049,0.0003760545,0.0003891388,0.0008907324,0.0002596843,0.0009807303,0.0003634807,0.0003161601,0.001124687],"category_scores_gemma":[0.004347572,0.000141686,0.0004574882,0.0006890259,0.000177646,0.0005566727,0.0006454678,0.0005935316,0.0002716053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006422781,"about_ca_system_score_gemma":0.0006167165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005543,"about_ca_topic_score_gemma":0.01225386,"domain_scores_codex":[0.9995771,0.0001954363,0.00001795578,0.0001318282,0.00003555641,0.00004207126],"domain_scores_gemma":[0.9989012,0.0005680491,0.0002151979,0.0001351229,0.00009506924,0.00008542261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001056972,0.0001433898,0.8502504,0.0001423497,0.0009264675,0.0001772192,0.0002396838,0.07355212,0.006416615,0.001292496,0.002643243,0.06315906],"study_design_scores_gemma":[0.00009734341,0.0005194636,0.4948646,0.000109863,0.001033867,0.000522225,0.0004554914,0.4731464,0.00438294,0.0162996,0.00849344,0.00007463692],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645402,0.001384006,0.0260183,0.0007569591,0.00004168311,0.00007681283,0.004867523,0.0002086439,0.002105775],"genre_scores_gemma":[0.9917744,0.0002281099,0.005225022,0.00007904097,0.00002504305,0.00004057142,0.002297561,0.00002333779,0.000307026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005543,"threshold_uncertainty_score":0.01102144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155165980433049,"score_gpt":0.3133528939119532,"score_spread":0.2918012341076227,"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."}}