{"id":"W2990588083","doi":"10.1177/0268580919885292","title":"Between personalized and racialized precision medicine: A relative resources perspective","year":2019,"lang":"en","type":"article","venue":"International Sociology","topic":"Race, Genetics, and Society","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; Ministry of Education - Singapore; Yale University","keywords":"Precision medicine; Personalized medicine; Context (archaeology); Health care; Perspective (graphical); Health informatics; Biomedicine; Informatics; Race (biology); Engineering ethics; Sociology; Medicine; Data science; Political science; Computer science; Bioinformatics; Law; Pathology; Engineering; Artificial intelligence; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01728019,0.0005154781,0.0005036559,0.002336232,0.006718428,0.00955443,0.001453868,0.003840564,0.004972813],"category_scores_gemma":[0.02083287,0.0003447073,0.000398443,0.001718127,0.0484512,0.01077166,0.009894419,0.004627539,0.0003153416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008676875,"about_ca_system_score_gemma":0.008343996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006968156,"about_ca_topic_score_gemma":0.005653361,"domain_scores_codex":[0.9810814,0.01397944,0.0002838766,0.001141431,0.001918625,0.001595231],"domain_scores_gemma":[0.9812945,0.01260861,0.001984379,0.001071505,0.0009533297,0.002087695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001617115,0.00001943065,0.001429886,0.00003018196,0.0000144058,0.00007668127,0.008626932,0.0002337529,0.00005091645,0.9825455,0.001124337,0.005831757],"study_design_scores_gemma":[0.0000282142,0.00005413003,0.003342892,0.000300209,0.00002632872,0.0003298471,0.02085799,0.0004806062,0.0001768548,0.9093572,0.06501745,0.0000283599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09866843,0.01290116,0.03395641,0.4522218,0.000746584,0.00007612572,0.0001288412,0.00003244161,0.4012682],"genre_scores_gemma":[0.9758515,0.002195475,0.002717595,0.01486177,0.0005549954,0.00007636548,0.00002075355,0.00002278399,0.00369878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01728019,"threshold_uncertainty_score":0.09138745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134489216316635,"score_gpt":0.3107250917328918,"score_spread":0.2993801995697254,"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."}}