{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009473879,0.001483817,0.003315885,0.01585216,0.0007479467,0.00752815,0.004027854,0.001765229,0.01730366],"category_scores_gemma":[0.03316492,0.0008396363,0.0009931878,0.01945891,0.0007749748,0.006331435,0.005352839,0.003847424,0.02567966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001393717,"about_ca_system_score_gemma":0.005859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003490465,"about_ca_topic_score_gemma":0.001975111,"domain_scores_codex":[0.9945314,0.001340996,0.001299393,0.0009389633,0.001578046,0.0003112729],"domain_scores_gemma":[0.9742743,0.005728071,0.002310252,0.006559401,0.00581457,0.005313384],"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":[0.001810736,0.0002426296,0.006544467,0.001480622,0.0002368953,0.0002347775,0.0001417426,0.0009368688,0.004874379,0.01466338,0.6682309,0.3006026],"study_design_scores_gemma":[0.00027691,0.000124636,0.01127684,0.0007295077,0.0002229833,0.0005712211,0.0001026149,0.003281968,0.005178542,0.009645113,0.9684711,0.0001185283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01294129,0.03020621,0.1086003,0.01795639,0.002516691,0.000748786,0.7310727,0.07095533,0.02500233],"genre_scores_gemma":[0.02492836,0.01399031,0.06951232,0.003358798,0.0010684,0.0006257918,0.8702611,0.01095875,0.005296098],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01730366,"threshold_uncertainty_score":0.05788654,"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."}}