{"id":"W4285077014","doi":"10.1158/2159-8290.cd-21-1547","title":"AACR Project GENIE: 100,000 Cases and Beyond","year":2022,"lang":"en","type":"article","venue":"Cancer Discovery","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"National Cancer Institute; Cancer Research UK; Children's Hospital of Philadelphia; Eli Lilly and Company; American Association for Cancer Research","keywords":"Milestone; Cancer; Genomics; Precision medicine; Resource (disambiguation); Clinical trial; Data science; Medicine; Genome; Bioinformatics; Computer science; Biology; Geography; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002870173,0.0009545767,0.0009466999,0.004693805,0.0007086844,0.003684526,0.001214618,0.00206797,0.03953109],"category_scores_gemma":[0.009662416,0.0004778338,0.0007465595,0.007131453,0.0005018485,0.001736085,0.002785317,0.002199624,0.02275885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002113576,"about_ca_system_score_gemma":0.003254495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02177523,"about_ca_topic_score_gemma":0.01560222,"domain_scores_codex":[0.9976566,0.0005151297,0.0001844705,0.0005369963,0.0008823801,0.0002244718],"domain_scores_gemma":[0.996334,0.0007284025,0.0006573577,0.0004457457,0.00101185,0.0008226361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00008996772,0.00001204563,0.002977795,0.0001433295,0.00003609906,0.00004054374,0.00001814631,0.0001967189,0.00006863436,0.001393738,0.9583695,0.0366535],"study_design_scores_gemma":[0.0001043033,0.000027662,0.01443933,0.0004162389,0.00005587051,0.0002758835,0.00005135447,0.000394757,0.0001488913,0.002427284,0.9816389,0.0000196236],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.008323091,0.03750802,0.006069933,0.05030024,0.007803468,0.0003797172,0.7564571,0.006339946,0.1268184],"genre_scores_gemma":[0.07296152,0.04252123,0.01232093,0.02072186,0.008040747,0.001518632,0.7623111,0.002507569,0.07709643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03953109,"threshold_uncertainty_score":0.1322446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376351194566876,"score_gpt":0.2690692035214192,"score_spread":0.2553056915757504,"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."}}