{"id":"W4416650860","doi":"10.5256/f1000research.11525.r19726","title":"Referee report. For: Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning [version 2; referees: 1 approved, 1 approved with reservations]","year":2017,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Breast cancer; Hormone therapy; Chemotherapy; Molecular taxonomy; Cancer; Alternative medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001173882,0.000158276,0.0003207859,0.00008135464,0.0001854313,0.0000261427,0.0005017889,0.00008275761,0.000003713785],"category_scores_gemma":[0.0006385946,0.0001080459,0.0000620023,0.0000936163,0.0004143704,0.00001783849,0.0002725347,0.0001780478,7.284637e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004328459,"about_ca_system_score_gemma":0.00008710982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004738034,"about_ca_topic_score_gemma":0.0005995151,"domain_scores_codex":[0.9983047,0.0001420293,0.0003792308,0.0003917329,0.00056742,0.0002148764],"domain_scores_gemma":[0.9978929,0.00008132622,0.0004652089,0.0005510046,0.0009720788,0.00003749899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001359146,0.0004318335,0.7771522,0.00003906459,0.0005546235,0.000002760615,0.0002058112,0.000007533232,0.2177811,0.000008421242,0.001343516,0.001114045],"study_design_scores_gemma":[0.005006211,0.0006914852,0.9009523,0.00007881918,0.00005451247,0.00001096842,0.002152765,0.0001131725,0.08384704,0.00001023658,0.006922558,0.000159864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919022,0.0004425679,0.0001600397,0.005498795,0.00001082519,0.0008870452,0.0008912486,0.00000434189,0.0002029629],"genre_scores_gemma":[0.9976308,0.0002496037,0.0007697741,0.00001524165,0.00002013828,0.0003309165,0.0005474719,0.00001741885,0.0004186491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.133934,"threshold_uncertainty_score":0.7162522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972512653364897,"score_gpt":0.3450539018291973,"score_spread":0.3053287752955484,"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."}}