{"id":"W2049444570","doi":"10.3747/co.20.1253","title":"Benefits, Issues, and Recommendations for Personalized Medicine in Oncology in Canada","year":2013,"lang":"en","type":"article","venue":"Current Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Centre for Reproductive Medicine; Vancouver General Hospital; University of British Columbia; Princess Margaret Cancer Centre; BC Cancer Agency; Jewish General Hospital; University Health Network; University of Toronto; University of Calgary; McGill University; University of Alberta","funders":"U.S. Food and Drug Administration; National Comprehensive Cancer Network","keywords":"Personalized medicine; Medicine; Precision medicine; Context (archaeology); Psychological intervention; SAFER; Genetic testing; Cancer prevention; Health care; MEDLINE; Alternative medicine; Family medicine; Cancer; Bioinformatics; Internal medicine; Nursing; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001487842,0.0001006859,0.0002300061,0.00005765092,0.00002659029,0.00000335434,0.00009006729,0.00009480905,0.0001332757],"category_scores_gemma":[0.0002238924,0.00009936441,0.00001737854,0.00006166282,0.00006702681,0.00000244533,0.00006444429,0.00009566567,0.000001490814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003877077,"about_ca_system_score_gemma":0.0009271828,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3591512,"about_ca_topic_score_gemma":0.9041533,"domain_scores_codex":[0.999162,0.00005066378,0.0002730399,0.0002494271,0.00003746838,0.0002274304],"domain_scores_gemma":[0.9995189,0.0001459326,0.00007482891,0.0001101698,0.00007103028,0.00007911074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001586706,0.0002924806,0.07015328,0.0000674968,0.00002875234,0.000002574947,0.0004634904,0.00007649039,0.00388165,0.003870675,0.4391654,0.481839],"study_design_scores_gemma":[0.002269276,0.0004532155,0.01509988,0.0000227593,0.000008217557,0.000005383329,0.0004935589,0.0001706674,0.0001741951,0.000369295,0.9808189,0.0001146492],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9369728,0.02870232,0.00009606128,0.03054196,0.001788342,0.0008149872,0.0001034077,0.000003384736,0.000976757],"genre_scores_gemma":[0.9524285,0.0397678,0.001566413,0.002523286,0.000994624,0.001078285,0.001121002,0.00003848884,0.0004816238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5450022,"threshold_uncertainty_score":0.6451163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05601928979221446,"score_gpt":0.3685454927805287,"score_spread":0.3125262029883142,"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."}}