{"id":"W2022033819","doi":"10.1186/gm109","title":"Coming of age of personalized medicine: challenges ahead","year":2009,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Biomedical Ethics and Regulation","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vlaamse regering","keywords":"Personalized medicine; Human genetics; Personal genomics; Genomics; Medicine; Sampling (signal processing); Precision medicine; Genomic medicine; Computational biology; Data science; Medical physics; Bioinformatics; Computer science; Biology; Genetics; Genome; Pathology; Gene","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":[0.07574886,0.001076685,0.002493357,0.001739853,0.01167763,0.01923769,0.0037308,0.03983348,0.01807175],"category_scores_gemma":[0.07952801,0.000697333,0.001723626,0.001928826,0.02166425,0.02867988,0.01316665,0.05072424,0.005186802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0113788,"about_ca_system_score_gemma":0.04864087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01942662,"about_ca_topic_score_gemma":0.03106219,"domain_scores_codex":[0.9668678,0.01387134,0.001793184,0.002007651,0.01132165,0.004138388],"domain_scores_gemma":[0.892619,0.06042708,0.002644267,0.003458124,0.02004336,0.02080828],"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":[0.0001119806,0.0001312646,0.0008836558,0.0005081406,0.00004952777,0.0003046524,0.002561304,0.0002910559,0.000369426,0.1788901,0.7275608,0.0883382],"study_design_scores_gemma":[0.00004179574,0.00006846122,0.001661097,0.001138333,0.00002897504,0.0004855197,0.007181311,0.0002602921,0.0002468437,0.1946616,0.79414,0.00008574326],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002517806,0.01455384,0.000954912,0.9779839,0.003966725,0.000006868888,0.00004661828,0.00002541613,0.002209894],"genre_scores_gemma":[0.0318537,0.05212653,0.00835334,0.8784342,0.02145196,0.0001030798,0.0002457352,0.0001299727,0.007301462],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07574886,"threshold_uncertainty_score":0.4006031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07236609730581829,"score_gpt":0.336772478837006,"score_spread":0.2644063815311877,"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."}}