{"id":"W4385342688","doi":"10.3390/cancers15153837","title":"Precision Medicine: Disease Subtyping and Tailored Treatment","year":2023,"lang":"en","type":"review","venue":"Cancers","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":275,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Precision medicine; Subtyping; Personalized medicine; Disease; Medicine; Perspective (graphical); MEDLINE; Alternative medicine; Medical physics; Data science; Computer science; Bioinformatics; Artificial intelligence; Pathology; 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":[],"consensus_categories":[],"category_scores_codex":[0.00006492124,0.0002756808,0.0005473191,0.00006260776,0.00005951455,0.00001829678,0.0001200673,0.0001482963,0.00001383879],"category_scores_gemma":[0.0001418827,0.0002130731,0.000152833,0.00009626854,0.00009525831,9.346011e-7,0.00009285869,0.00005581723,0.00001416512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000474584,"about_ca_system_score_gemma":0.001114789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001398866,"about_ca_topic_score_gemma":0.00009673921,"domain_scores_codex":[0.9989839,0.00002940742,0.0002460543,0.0004694329,0.00007594176,0.0001953314],"domain_scores_gemma":[0.9992161,0.00005915941,0.0001293343,0.0003631398,0.00002628216,0.0002059585],"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":[0.00003268014,0.000003881712,0.000007235717,0.002116189,0.0001790601,0.00001633446,0.00002087757,0.0000357636,0.00001219856,0.00003778497,0.01025325,0.9872847],"study_design_scores_gemma":[0.0002568857,0.0001751048,0.000006846495,0.002534133,0.0004674925,0.000003144933,0.00001244352,0.00001212627,0.000005203627,0.00003601084,0.9962695,0.0002211633],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001008041,0.9984679,0.00002696259,0.00002952971,0.0005243373,0.0004646412,0.0001630875,0.00001338456,0.0002093785],"genre_scores_gemma":[0.0000423907,0.9969631,0.00001753063,0.00005918282,0.0008989825,0.0001932811,0.0007021124,0.00006108407,0.001062329],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9870636,"threshold_uncertainty_score":0.8688871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0467402518210434,"score_gpt":0.3434385200122294,"score_spread":0.296698268191186,"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."}}