{"id":"W4362557621","doi":"10.1158/2159-8290.c.6549518","title":"Data from AACR Project GENIE: 100,000 Cases and Beyond","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Milestone; Cancer; Clinical trial; Resource (disambiguation); Genomics; Precision medicine; Medicine; Political science; Genome; Computer science; Biology; Geography; Genetics; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00394959,0.0009887816,0.001401901,0.005018583,0.0007295291,0.002803881,0.001553769,0.001783067,0.03710072],"category_scores_gemma":[0.02079981,0.0008315576,0.001311571,0.01042142,0.000459413,0.001565293,0.00262987,0.001984505,0.02024003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002406945,"about_ca_system_score_gemma":0.003583807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04567174,"about_ca_topic_score_gemma":0.04831641,"domain_scores_codex":[0.9947433,0.001158977,0.0009461913,0.001380642,0.001356048,0.0004148817],"domain_scores_gemma":[0.9871656,0.003038937,0.002570591,0.002709753,0.003242361,0.001272764],"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":[0.0005814538,0.00003508274,0.0156904,0.0008190231,0.0003165364,0.00009757072,0.0000783268,0.001007985,0.000188047,0.001108801,0.9564067,0.02366998],"study_design_scores_gemma":[0.0006440725,0.00009284976,0.07863592,0.001495935,0.0003666288,0.0004790048,0.0001936492,0.0007265948,0.0005389481,0.002312948,0.9144323,0.000081165],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00303366,0.001606567,0.0008636657,0.001608198,0.0001996627,0.0001147896,0.9818456,0.0007188025,0.01000908],"genre_scores_gemma":[0.01717942,0.001355201,0.001351677,0.00175237,0.0002217785,0.0005303588,0.9725949,0.0004552037,0.004559077],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04567174,"threshold_uncertainty_score":0.1241143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07935627957879894,"score_gpt":0.3307564022680054,"score_spread":0.2514001226892065,"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."}}