{"id":"W4233227002","doi":"10.12688/f1000research.9417.1","title":"Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning","year":2016,"lang":"en","type":"preprint","venue":"F1000Research","topic":"HER2/EGFR in Cancer Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Windsor","funders":"","keywords":"Medicine; Breast cancer; Oncology; Internal medicine; Biology; Cancer","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":[0.002784268,0.0003049299,0.0008437131,0.0004949946,0.00004848345,0.00004416599,0.0009188873,0.0002230842,0.0002916752],"category_scores_gemma":[0.0006613645,0.0002017137,0.0001429151,0.0003511368,0.0005329751,0.00004647956,0.001042997,0.001671729,0.000002065816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002838924,"about_ca_system_score_gemma":0.0005773716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005541496,"about_ca_topic_score_gemma":0.00005607165,"domain_scores_codex":[0.9954385,0.0005814212,0.000808161,0.0006540672,0.002025321,0.0004925088],"domain_scores_gemma":[0.9977213,0.0005411772,0.0003130792,0.0006879991,0.000579552,0.000156854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007380205,0.000686161,0.9111058,0.0002503355,0.0006766087,0.00003782014,0.0005417809,0.000007166045,0.07770049,0.00001277452,0.0007783408,0.007464772],"study_design_scores_gemma":[0.006413999,0.0005616782,0.9672881,0.0005843825,0.00009957673,0.00002500046,0.0008854511,0.001513359,0.02030799,0.00006082874,0.001975795,0.0002838347],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831123,0.004775895,0.0001148772,0.00749992,0.00007167514,0.003006538,0.0006121511,0.00002758023,0.0007790978],"genre_scores_gemma":[0.9954763,0.002744253,0.0001610137,0.00009715425,0.00009849779,0.0009118184,0.00006516743,0.00006582934,0.0003799768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0573925,"threshold_uncertainty_score":0.8377122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04348960773477192,"score_gpt":0.3747409645581481,"score_spread":0.3312513568233763,"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."}}