{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002845037,0.0009287533,0.00212457,0.002888068,0.0004051669,0.002103675,0.001300041,0.001911683,0.003345936],"category_scores_gemma":[0.003547664,0.0003324895,0.001256972,0.001891373,0.001555396,0.002014517,0.001361655,0.003845559,0.001641106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001716521,"about_ca_system_score_gemma":0.002680159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425645,"about_ca_topic_score_gemma":0.002076974,"domain_scores_codex":[0.9986727,0.0004543136,0.0001329145,0.0001921435,0.0004596396,0.00008830197],"domain_scores_gemma":[0.9979845,0.001354245,0.0001604712,0.0001123816,0.0003199665,0.00006841244],"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":[0.00007163151,0.00006709549,0.0004480903,0.01777196,0.0003203775,0.0003445856,0.0001547685,0.001235974,0.002247782,0.056662,0.04614441,0.8745314],"study_design_scores_gemma":[0.00002276998,0.0000771002,0.000645754,0.006653121,0.0001573431,0.001331954,0.00008254575,0.0004776783,0.001098832,0.02865656,0.9607528,0.00004349544],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000242086,0.9847913,0.004726473,0.005171963,0.0009382364,0.00003056772,0.0001050783,0.00005801259,0.003936254],"genre_scores_gemma":[0.004230076,0.9878035,0.00320526,0.002438422,0.001000378,0.00004599314,0.0001501739,0.000008936975,0.001117317],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003345936,"threshold_uncertainty_score":0.01504618,"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."}}