{"id":"W2106547072","doi":"10.1038/gim.2014.35","title":"Framework for development of physician competencies in genomic medicine: report of the Competencies Working Group of the Inter-Society Coordinating Committee for Physician Education in Genomics","year":2014,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":150,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Genetics; National Human Genome Research Institute; National Heart, Lung, and Blood Institute","keywords":"Genomics; Pace; Genomic medicine; Best practice; Relevance (law); Medicine; Personalized medicine; Medical education; Engineering ethics; Bioinformatics; Genome; Genetics; Management; Engineering; Computational biology; Political science; Biology","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":[0.001056125,0.0001525975,0.0003792697,0.00006765335,0.0000590743,0.000002118658,0.0004901085,0.0001143332,8.488097e-7],"category_scores_gemma":[0.0003992716,0.0001110125,0.00008813755,0.0003053241,0.0004874041,0.000002249021,0.0001698727,0.0001482927,2.519118e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001598118,"about_ca_system_score_gemma":0.0004226829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005998268,"about_ca_topic_score_gemma":0.0009586211,"domain_scores_codex":[0.9984184,0.0000800141,0.0008788902,0.0002559354,0.0001749137,0.0001918673],"domain_scores_gemma":[0.9984153,0.0002176818,0.0006628393,0.0005027578,0.0001843324,0.00001709638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005613809,0.0001917672,0.07367168,0.0005120979,0.00008150203,7.78584e-8,0.01163323,0.002563503,0.8408538,0.002648214,0.0005612809,0.06722672],"study_design_scores_gemma":[0.004939646,0.0008211237,0.3657767,0.006041541,0.0001683777,0.000008114224,0.03516607,0.006158224,0.5028566,0.02768801,0.04970914,0.0006664079],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620101,0.001470365,0.03416134,0.0007358655,0.0006424589,0.0007803072,0.000006183464,0.000001771077,0.0001916477],"genre_scores_gemma":[0.9186989,0.00006640606,0.08026182,0.0005263534,0.0002776771,0.00009545107,0.00003490737,0.00002133384,0.00001715956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3379972,"threshold_uncertainty_score":0.4526956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02334943850063738,"score_gpt":0.3056736476872337,"score_spread":0.2823242091865963,"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."}}