{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002993839,0.0001285706,0.0002063959,0.00003618252,0.00008502937,0.000009248937,0.0001884992,0.0002686783,0.0002294648],"category_scores_gemma":[0.0003519499,0.0001119355,0.00009160103,0.00002508218,0.0006498332,0.000005648264,0.0001292024,0.000142803,0.00002784479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004889564,"about_ca_system_score_gemma":0.00003369688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005329484,"about_ca_topic_score_gemma":0.00000439132,"domain_scores_codex":[0.9989523,0.0001495505,0.0001843318,0.0004063479,0.0001566886,0.0001507886],"domain_scores_gemma":[0.9993515,0.0001266285,0.0001141022,0.0001423308,0.0002105498,0.00005489191],"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":[0.001339233,0.0001351369,0.4181596,0.00003037805,0.002612714,0.000007134516,0.1180417,0.00003439733,0.3624144,0.0780564,0.01517921,0.003989665],"study_design_scores_gemma":[0.009459689,0.001971046,0.2565552,0.00007601384,0.0001463172,0.00003693723,0.03375663,0.0002086033,0.004348027,0.07804799,0.6145307,0.0008628479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794875,0.001996395,0.0006740635,0.003293272,0.0003838003,0.0001741512,0.00002442196,0.00001159821,0.0139548],"genre_scores_gemma":[0.9907255,0.0005838216,0.0004845275,0.0005189005,0.000878923,0.00001172793,0.0001001531,0.00001473867,0.006681727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5993515,"threshold_uncertainty_score":0.4564599,"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."}}