{"id":"W2906821085","doi":"10.1016/j.ajhg.2018.11.014","title":"Integrating Genomics into Healthcare: A Global Responsibility","year":2019,"lang":"en","type":"article","venue":"The American Journal of Human Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":395,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Genomics; Ontario Institute for Cancer Research","funders":"Medical Research Council; National Institute for Health and Care Research; Health Data Research UK; National Health and Medical Research Council; Australian Genomics Health Alliance; Cancer Research UK; Wellcome Trust","keywords":"Transformative learning; Government (linguistics); Health care; Precision medicine; Data sharing; Diversity (politics); Genomics; Business; Personalized medicine; Political science; Economic growth; Medicine; Bioinformatics; Sociology; Biology; Genome; Alternative medicine; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05061609,0.001548019,0.002292037,0.002301075,0.003769974,0.01835325,0.003418457,0.01960996,0.02025872],"category_scores_gemma":[0.04588111,0.0008084648,0.001224492,0.003139083,0.01342054,0.01943903,0.01750006,0.0246808,0.006213788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006390888,"about_ca_system_score_gemma":0.03764237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003396927,"about_ca_topic_score_gemma":0.003223172,"domain_scores_codex":[0.9739512,0.01135321,0.00160431,0.002207527,0.007582976,0.003300685],"domain_scores_gemma":[0.8755636,0.03110818,0.005437093,0.01029554,0.02731187,0.05028363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001096626,0.0002950291,0.002896663,0.0005969051,0.0002352412,0.0003502374,0.001577294,0.001092434,0.001540245,0.2156738,0.6108614,0.164771],"study_design_scores_gemma":[0.00005103774,0.0001060503,0.003025288,0.001075775,0.00007227051,0.0004885236,0.002898499,0.0008791529,0.0004080234,0.1635916,0.8273033,0.0001003848],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0009415331,0.01336998,0.009649932,0.9463486,0.009339091,0.00002856421,0.00008220614,0.0002339701,0.02000617],"genre_scores_gemma":[0.181548,0.07754782,0.04429526,0.6013232,0.05043436,0.0001862589,0.000862963,0.0009173999,0.04288465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05061609,"threshold_uncertainty_score":0.2676867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008103850877362772,"score_gpt":0.2919829303738561,"score_spread":0.2838790794964934,"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."}}