{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004983757,0.0008144187,0.001771063,0.002595187,0.0004495742,0.003709138,0.001450441,0.002272685,0.009578872],"category_scores_gemma":[0.01072192,0.0005436075,0.0008312284,0.00147751,0.001101787,0.003051507,0.002380384,0.003681785,0.003354147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010329,"about_ca_system_score_gemma":0.0013046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001669842,"about_ca_topic_score_gemma":0.002334323,"domain_scores_codex":[0.9972633,0.0009292709,0.0001499238,0.0005535719,0.0008659403,0.000238033],"domain_scores_gemma":[0.9904947,0.004873108,0.0007450107,0.001267149,0.001935399,0.0006845703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008341792,0.0002353736,0.04026283,0.002661629,0.0004520702,0.0005634077,0.0004227446,0.001938535,0.1110023,0.0199238,0.08171161,0.7399915],"study_design_scores_gemma":[0.0002153939,0.001176228,0.0804683,0.002493646,0.001091772,0.007486662,0.001014289,0.008430108,0.07472207,0.09182204,0.7307542,0.0003253827],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"commentary","genre_scores_codex":[0.04588488,0.4196123,0.309182,0.1634604,0.004914389,0.0002808324,0.01018785,0.006672856,0.03980459],"genre_scores_gemma":[0.4254966,0.256447,0.2116062,0.06799083,0.01265178,0.0004348384,0.008726398,0.001733157,0.01491317],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.009578872,"threshold_uncertainty_score":0.03204453,"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."}}