{"id":"W2146688302","doi":"10.1373/clinchem.2011.181073","title":"Personalized Cancer Genomics: The Road Map to Clinical Implementation","year":2012,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Personalized medicine; Precision medicine; Genomics; Profiling (computer programming); Computational biology; Data science; Disease; Genomic information; Bioinformatics; Biology; Genome; Computer science; Medicine; Genetics; Pathology; Gene","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":[0.0009227619,0.0001621503,0.0002176478,0.000005392157,0.00009422607,0.00003222951,0.0003234958,0.0002284735,0.0004073464],"category_scores_gemma":[0.0002975379,0.0001319549,0.0002744563,0.00004641703,0.0001691263,0.00000317011,0.0002537711,0.000212814,0.00008339185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004178473,"about_ca_system_score_gemma":0.0002091642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003521591,"about_ca_topic_score_gemma":0.00002623915,"domain_scores_codex":[0.9984332,0.00006837134,0.0006312926,0.0003741821,0.0001165189,0.0003764883],"domain_scores_gemma":[0.9988015,0.0001211254,0.0001581067,0.0004926445,0.00008227309,0.0003443189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003704815,0.0003480668,0.6637631,0.00004171748,0.0002895251,0.000001713411,0.0001488016,0.00001183049,0.05222056,0.00005683194,0.2307249,0.05202248],"study_design_scores_gemma":[0.001038342,0.00008943704,0.03847554,0.000007681918,0.0000737556,0.000003734187,0.0002848788,0.000008940397,0.03901773,0.00002410127,0.9207188,0.0002570511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926956,0.002943722,0.0001316699,0.002169313,0.001177641,0.000225324,0.000115205,0.00001078571,0.0005307152],"genre_scores_gemma":[0.9829366,0.002092118,0.0003937098,0.004678099,0.008135848,0.0001007934,0.0001841652,0.00003473994,0.001443952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6899939,"threshold_uncertainty_score":0.5380965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0520625158581419,"score_gpt":0.4285138058584388,"score_spread":0.3764512900002969,"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."}}