{"id":"W1972506346","doi":"10.2174/1573400510666140630170549","title":"Jumping on the Train of Personalized Medicine: A Primer for Non- Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area","year":2014,"lang":"en","type":"article","venue":"Current Psychiatry Reviews","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Personalized medicine; Geneticist; Precision medicine; Personal genomics; Medicine; Mendelian inheritance; Data science; Computer science; Bioinformatics; Whole genome sequencing; Genetics; Biology; Genome; Pathology","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":[0.0191598,0.001300266,0.002183902,0.001658782,0.003148502,0.01165648,0.002172393,0.01735263,0.007501246],"category_scores_gemma":[0.02265465,0.0009845237,0.001066352,0.0011287,0.01104682,0.0167889,0.007265302,0.02587166,0.005247529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002866562,"about_ca_system_score_gemma":0.00915866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001320907,"about_ca_topic_score_gemma":0.002567453,"domain_scores_codex":[0.9905249,0.005591082,0.0008349773,0.0005654249,0.002047567,0.000436003],"domain_scores_gemma":[0.9801822,0.01327005,0.001027761,0.000699251,0.002663735,0.002156964],"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":[0.0001265516,0.0003601965,0.001409543,0.003945859,0.00008321835,0.0009806355,0.005026599,0.0004156004,0.002497325,0.05988976,0.5095447,0.41572],"study_design_scores_gemma":[0.00004428476,0.000208573,0.001337104,0.004755096,0.00003721534,0.002261603,0.002959323,0.0003042215,0.0004770974,0.07579798,0.9117313,0.00008623367],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.0004616128,0.2337054,0.01229515,0.7256904,0.01973351,0.0001742949,0.00003919239,0.0001766734,0.007723835],"genre_scores_gemma":[0.01478873,0.4330995,0.05174983,0.4358967,0.04849698,0.0009751626,0.0001257463,0.0002299882,0.01463738],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.0191598,"threshold_uncertainty_score":0.101328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1223735242661169,"score_gpt":0.4194591586625058,"score_spread":0.2970856343963889,"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."}}