{"id":"W2606926876","doi":"10.1038/srep46349","title":"Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":277,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Radiomics; Feature selection; Redundancy (engineering); Random forest; Lung cancer; Feature (linguistics); Artificial intelligence; Computer science; Medicine; Pattern recognition (psychology); Oncology","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":[0.001826328,0.0001680577,0.0004513555,0.000465319,0.000988499,0.0005762182,0.000232467,0.00007977305,0.0001200164],"category_scores_gemma":[0.0005785862,0.0001398989,0.0004365484,0.0003990652,0.0003144299,0.00008930449,0.00005599873,0.0002020663,0.000002546157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009628225,"about_ca_system_score_gemma":0.0004842303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002205206,"about_ca_topic_score_gemma":0.00003623172,"domain_scores_codex":[0.9978816,0.00001597691,0.0004545981,0.0008300446,0.0004000731,0.0004176975],"domain_scores_gemma":[0.9973819,0.0000500881,0.0006063131,0.001391603,0.0002687285,0.0003013427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002763526,0.0001425216,0.9579125,0.0002859924,0.0003264614,0.0002944096,0.0001513318,0.001111703,0.01442695,0.000006679339,0.02002202,0.005291822],"study_design_scores_gemma":[0.001928932,0.00007732679,0.1289915,0.0002922095,0.004482311,0.00005670454,0.00003612032,0.7388674,0.04654443,0.0002388742,0.07800521,0.0004790128],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581801,0.0003044317,0.02947401,0.00336574,0.00530533,0.001082002,0.000006660791,0.00006812902,0.002213645],"genre_scores_gemma":[0.9792932,0.000008846494,0.01118766,0.0002250181,0.0002559935,0.0001901669,0.000097931,0.00003035746,0.008710871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.828921,"threshold_uncertainty_score":0.7602836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683635680560899,"score_gpt":0.3190334353009178,"score_spread":0.3021970784953088,"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."}}