{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009435716,0.0003323507,0.0002988458,0.0007227021,0.0003123126,0.0005263306,0.0003054273,0.0003193616,0.0004229586],"category_scores_gemma":[0.002110717,0.0001304384,0.0002477447,0.0006576148,0.000139482,0.0002088526,0.0005895661,0.0003313121,0.0001879445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005991734,"about_ca_system_score_gemma":0.0006344839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01416791,"about_ca_topic_score_gemma":0.02151993,"domain_scores_codex":[0.9996953,0.0001281116,0.00002344512,0.00006997177,0.00004881054,0.00003426856],"domain_scores_gemma":[0.9992083,0.0001428785,0.0002803297,0.000153497,0.0001248852,0.00009026748],"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":[0.0002961706,0.00004463936,0.9894275,0.000008536097,0.00005323468,0.00004197914,0.00005636103,0.0008780034,0.0007403154,0.00006310709,0.0005818841,0.007808256],"study_design_scores_gemma":[0.00004449939,0.000286193,0.9814824,0.00001155722,0.00009255871,0.0003786982,0.0001796912,0.01415383,0.001131994,0.0001617643,0.002064391,0.00001237946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974137,0.0001311971,0.0007709454,0.00008143229,0.000005091771,0.00002945499,0.001118628,0.00002123527,0.000428304],"genre_scores_gemma":[0.9945301,0.00006630566,0.002174652,0.0000525597,0.000009610835,0.00003715688,0.002783128,0.000008586849,0.000337915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01416791,"threshold_uncertainty_score":0.02817088,"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."}}