{"id":"W4365138057","doi":"10.1089/ipm.10.02.08","title":"Genomic Profiling for Lung Cancer: Improving Access to this Important Tool","year":2023,"lang":"en","type":"article","venue":"Inside Precision Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"CARE Canada","funders":"","keywords":"Lung cancer; Profiling (computer programming); Precision medicine; Download; Medicine; Personalized medicine; Health care; Internal medicine; Oncology; Bioinformatics; Pathology; Computer science; World Wide Web; Biology; Political science","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":[0.0005458049,0.0001763724,0.0002409917,0.0001373075,0.0001150157,0.00004127893,0.000334826,0.000111451,0.00006307047],"category_scores_gemma":[0.00130942,0.0001507364,0.00006467118,0.0002198433,0.00003290028,0.00000605645,0.0003841601,0.00007858634,0.000007166103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000626734,"about_ca_system_score_gemma":0.000226899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000277377,"about_ca_topic_score_gemma":0.0001839375,"domain_scores_codex":[0.9985098,0.00001542028,0.0004558866,0.0004953875,0.0001806825,0.0003428223],"domain_scores_gemma":[0.9990001,0.0001433008,0.0001334144,0.0004146284,0.0001612378,0.0001473727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002558098,0.00001207328,0.01686833,0.00007693809,0.00004130131,0.000008297759,0.0001162701,0.001207715,0.8279277,0.00005577513,0.1049375,0.04849231],"study_design_scores_gemma":[0.005018482,0.001649803,0.02651587,0.0004862379,0.0002587727,0.00002413369,0.0002402515,0.03119136,0.5958318,0.001113961,0.33646,0.001209352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852874,0.003071798,0.0062797,0.002268199,0.001375659,0.001260224,0.0001515445,0.00004612455,0.0002593964],"genre_scores_gemma":[0.9782302,0.005309312,0.004675754,0.005534288,0.003612741,0.0008991371,0.0005721512,0.0001180402,0.001048327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2320959,"threshold_uncertainty_score":0.6146853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572829336250206,"score_gpt":0.3421582136576449,"score_spread":0.3164299202951428,"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."}}