{"id":"W4220705085","doi":"10.1038/s41418-022-00976-3","title":"The TP53 Database: transition from the International Agency for Research on Cancer to the US National Cancer Institute","year":2022,"lang":"en","type":"article","venue":"Cell Death and Differentiation","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":165,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health; Centre International de Recherche sur le Cancer; World Health Organization","keywords":"International agency; Agency (philosophy); Cancer; Political science; Database; Transition (genetics); Medicine; Sociology; Computer science; Social science; Internal medicine; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002920331,0.00006600362,0.00003806267,0.0000196105,0.0008821503,0.00008658562,0.0002359435,0.00002369279,0.00004690309],"category_scores_gemma":[0.00006128489,0.0000401745,0.00003440283,0.00005453329,0.00002906248,0.000004313416,0.000118659,0.00009899614,0.000001391937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007413534,"about_ca_system_score_gemma":0.0001462004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007611653,"about_ca_topic_score_gemma":0.002159176,"domain_scores_codex":[0.9992407,0.00005209421,0.00009857986,0.0002079855,0.0002714686,0.0001291739],"domain_scores_gemma":[0.9995225,0.0001296037,0.00003730841,0.0001502713,0.0001277995,0.00003248582],"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":[0.002893081,0.0004848768,0.01088235,0.00003996145,0.0004141281,0.000001756131,0.002571397,0.04816001,0.3968472,0.02396232,0.4434586,0.07028428],"study_design_scores_gemma":[0.0008535323,0.000165155,0.04211998,0.00001060311,0.00003726558,5.89891e-7,0.0001723771,0.005468587,0.01801542,0.001571355,0.9314438,0.0001413375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808838,0.002145031,0.002811729,0.009442706,0.001377816,0.0004904834,0.002523927,0.000003088465,0.000321454],"genre_scores_gemma":[0.9919167,0.002338899,0.00002479493,0.001989884,0.0009506549,0.0008217188,0.001582285,0.000008436404,0.0003666579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4879852,"threshold_uncertainty_score":0.6784876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05226125172015068,"score_gpt":0.3384759551490655,"score_spread":0.2862147034289149,"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."}}