{"id":"W2601571564","doi":"10.1200/jco.2013.31.15_suppl.tps656","title":"OPTIMA prelim: Optimal personalized treatment of early breast cancer using multiparameter tests.","year":2013,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Ontario Institute for Cancer Research","funders":"","keywords":"Medicine; Concordance; Breast cancer; Randomized controlled trial; Oncology; Population; Test (biology); Personalized medicine; Internal medicine; Cancer; Gynecology; Bioinformatics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006492829,0.001197766,0.00200942,0.0006072518,0.0001939698,0.00144524,0.001218572,0.001545834,0.01245447],"category_scores_gemma":[0.01299487,0.001099546,0.002014522,0.0004789102,0.0007347518,0.0009364773,0.001355537,0.002185059,0.001197775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001812394,"about_ca_system_score_gemma":0.003086691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002650338,"about_ca_topic_score_gemma":0.002543833,"domain_scores_codex":[0.9974369,0.00197767,0.00005208389,0.0001751331,0.0002047599,0.0001534191],"domain_scores_gemma":[0.993099,0.005762772,0.0005147225,0.0001537688,0.0002184748,0.0002513147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003780495,0.0002614252,0.001890893,0.0007328404,0.0004129719,0.0001113197,0.0000596819,0.8773903,0.0006711573,0.01585937,0.007921927,0.09090768],"study_design_scores_gemma":[0.001772919,0.002358624,0.001829538,0.0002791722,0.0004605991,0.0001741913,0.00004735179,0.9456286,0.001096287,0.03719808,0.009099863,0.00005474548],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.084223,0.008280796,0.8519419,0.0101019,0.0004601688,0.004765394,0.004212897,0.001935738,0.03407814],"genre_scores_gemma":[0.7629827,0.002122793,0.2132846,0.002166132,0.000209487,0.00411859,0.001147697,0.0002863935,0.01368161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01245447,"threshold_uncertainty_score":0.04166436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09235203774182987,"score_gpt":0.4345221743203548,"score_spread":0.342170136578525,"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."}}