{"id":"W2047595465","doi":"10.2174/187569209790112283","title":"Editorial [Personalized Medicine Beyond Genomics: New Technologies, Global Health Diplomacy and Anticipatory Governance]","year":2009,"lang":"en","type":"article","venue":"Current pharmacogenomics and personalized medicine (Online)/Current pharmacogenomics and personalized medicine","topic":"Science, Research, and Medicine","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Canadian Institutes of Health Research; U.S. Public Health Service","keywords":"Personalized medicine; Pharmacogenomics; Precision medicine; Nutrigenomics; Data science; Medicine; Genomics; Health care; Computer science; Bioinformatics; Political science; Genome; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.003630212,0.002330322,0.0045324,0.001015376,0.001123757,0.0001088274,0.001076394,0.0005450288,0.000939403],"category_scores_gemma":[0.0009940235,0.001805886,0.0004428888,0.001630352,0.007122023,0.0004951763,0.000535101,0.002908706,0.00001258286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001351393,"about_ca_system_score_gemma":0.0027997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006622404,"about_ca_topic_score_gemma":0.00003649717,"domain_scores_codex":[0.9874068,0.0004479104,0.003025979,0.003266125,0.003013737,0.002839369],"domain_scores_gemma":[0.9910474,0.0006242091,0.001566019,0.0009769027,0.0008334419,0.004951973],"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":[0.006644868,0.001284955,0.003662043,0.002024978,0.0008883734,0.0001574666,0.01025338,0.000002074762,0.05243502,0.007122421,0.2602762,0.6552482],"study_design_scores_gemma":[0.07066207,0.003597798,0.003443662,0.00336824,0.002707841,0.001142131,0.004610411,0.00234594,0.0001267116,0.002987234,0.9033564,0.001651575],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2578018,0.6374907,0.0005979814,0.06984697,0.02771172,0.003869272,0.001541359,0.0005308485,0.0006092923],"genre_scores_gemma":[0.10233,0.8069538,0.0008531616,0.01279907,0.07349712,0.00009507298,0.002457924,0.0002379185,0.000775896],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.6535966,"threshold_uncertainty_score":0.9999739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07698144112303296,"score_gpt":0.4366302173809588,"score_spread":0.3596487762579259,"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."}}