{"id":"W4389839591","doi":"10.1016/j.jtocrr.2023.100623","title":"Brief Report: Comprehensive Clinicogenomic Profiling of Small Cell Transformation From EGFR-Mutant NSCLC Informs Potential Therapeutic Targets","year":2023,"lang":"en","type":"article","venue":"JTO Clinical and Research Reports","topic":"Lung Cancer Research Studies","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Genentech; National Institutes of Health; Mirati Therapeutics; Regeneron Pharmaceuticals; Daiichi Sankyo Europe; Sanofi; GlaxoSmithKline; Eli Lilly and Company; AstraZeneca; Damon Runyon Cancer Research Foundation; LUNGevity Foundation; Computer Modelling Group; Amgen; Cancer Prevention and Research Institute of Texas; University of Texas MD Anderson Cancer Center; V Foundation for Cancer Research","keywords":"Profiling (computer programming); Mutant; Transformation (genetics); Cancer research; Oncology; Computational biology; Medicine; Biology; Computer science; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0005148199,0.0003288334,0.0003500509,0.000801225,0.0003475452,0.0009609262,0.0002629867,0.000571123,0.005023886],"category_scores_gemma":[0.0008220976,0.0001514007,0.0002239094,0.0006670627,0.0001872394,0.0003309181,0.0004111238,0.0004368428,0.002022983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003399831,"about_ca_system_score_gemma":0.0004821534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004531479,"about_ca_topic_score_gemma":0.0009325977,"domain_scores_codex":[0.9997116,0.00004059659,0.00003256038,0.00008560564,0.00007829504,0.00005139947],"domain_scores_gemma":[0.999194,0.0001312653,0.0001483235,0.00005996784,0.0002301654,0.0002364326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"case_report","study_design_scores_codex":[0.002202727,0.0002701461,0.3734012,0.001224853,0.0002937909,0.01680312,0.0004664081,0.0005953709,0.1124507,0.0007673196,0.2317284,0.2597959],"study_design_scores_gemma":[0.0001142241,0.00125326,0.6390106,0.0002127203,0.0003664802,0.05889466,0.0007786449,0.001240967,0.03109156,0.0008937771,0.2660468,0.00009622535],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628286,0.03993579,0.01043492,0.0152868,0.005645889,0.0005426205,0.04452401,0.001614056,0.01918724],"genre_scores_gemma":[0.9350532,0.01441443,0.005252355,0.002712343,0.005239313,0.000173245,0.02670089,0.000182355,0.01027194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005023886,"threshold_uncertainty_score":0.0168066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1404393992648889,"score_gpt":0.4482286477040534,"score_spread":0.3077892484391644,"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."}}