{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001008444,0.0001705946,0.0008978628,0.000285908,0.00003516545,6.490673e-7,0.0001261375,0.0002112572,0.0006467191],"category_scores_gemma":[0.0002545039,0.0001077247,0.00008719955,0.0003208772,0.002714342,0.00002263268,0.00001987634,0.0002688469,0.000003104684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003432051,"about_ca_system_score_gemma":0.00007176599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005478614,"about_ca_topic_score_gemma":0.000004812507,"domain_scores_codex":[0.9979911,0.00005596594,0.0006780057,0.0002408009,0.0008107157,0.0002233899],"domain_scores_gemma":[0.9988145,0.0001253861,0.000256181,0.0003436907,0.0002369392,0.0002233667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005358482,0.0003248938,0.0001468819,0.00153144,0.000191226,0.0001116564,0.01810524,0.000002008981,0.7841948,0.06198994,0.000756559,0.1321095],"study_design_scores_gemma":[0.03718217,0.02070009,0.6056913,0.01018546,0.001758607,0.0004419512,0.01221054,0.0005427603,0.004913973,0.05671764,0.2488662,0.000789389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1575877,0.1419543,0.003182194,0.6191175,0.0008243256,0.001229226,0.00001283886,0.0001365549,0.07595538],"genre_scores_gemma":[0.9911661,0.005520413,0.000556614,0.001172308,0.0006732666,0.000002282651,0.00008443183,0.00001411837,0.000810429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8335785,"threshold_uncertainty_score":0.9999997,"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."}}