{"id":"W2514228312","doi":"10.12688/f1000research.9417.3","title":"Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning","year":2017,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cytodiagnostics (Canada); University of Windsor; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Breast cancer; Biology; Medicine; Oncology; Internal medicine; Cancer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007616252,0.000179726,0.0002930805,0.0001165339,0.00006051759,0.00004715485,0.0008295809,0.0001786406,0.00002135819],"category_scores_gemma":[0.0001616368,0.0001387295,0.00008271862,0.00006562137,0.0002063982,0.000004430322,0.0006363298,0.0004253576,2.131349e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002476027,"about_ca_system_score_gemma":0.000146575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001033178,"about_ca_topic_score_gemma":0.00003423246,"domain_scores_codex":[0.9982876,0.0002448007,0.0003770446,0.0004497541,0.0004626755,0.0001781495],"domain_scores_gemma":[0.9987451,0.00003119272,0.0003830009,0.0005758673,0.0002162724,0.00004850372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001627108,0.0002137675,0.4661319,0.00002652377,0.0001634158,9.984669e-7,0.0001086122,0.00002916534,0.5299318,0.000003845267,0.0006018787,0.00262538],"study_design_scores_gemma":[0.00227316,0.0002072464,0.7806969,0.00006789794,0.00004220867,0.000003800877,0.0006832943,0.0009851055,0.2097787,0.000024495,0.004986966,0.0002502369],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929966,0.004227738,0.0002027509,0.001205033,0.00006539401,0.000815932,0.0002330248,0.000005219421,0.0002482691],"genre_scores_gemma":[0.9960927,0.002879085,0.00009231768,0.00004606081,0.00005168941,0.0003922702,0.0001897864,0.00002302243,0.0002330758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3201531,"threshold_uncertainty_score":0.5657225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095765592080526,"score_gpt":0.3360658616825897,"score_spread":0.3051082057617845,"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."}}