{"id":"W4293659595","doi":"10.3390/cancers14174150","title":"Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation","year":2022,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute","keywords":"Medicine; Stage (stratigraphy); Lung cancer; TNM staging system; Risk stratification; Lung; Radiology; Internal medicine; Oncology; Receiver operating characteristic; Cancer; Neoplasm staging","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":[0.001743403,0.000860391,0.0007484241,0.001117014,0.0001633091,0.0007779789,0.0006158897,0.0004564519,0.0006154616],"category_scores_gemma":[0.003788998,0.0002094765,0.000733776,0.0006024405,0.0002350002,0.000613635,0.000848076,0.0007034822,0.000178308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008879141,"about_ca_system_score_gemma":0.0009682278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00517218,"about_ca_topic_score_gemma":0.006790502,"domain_scores_codex":[0.9993126,0.0002399994,0.00005671366,0.0001561858,0.0001352231,0.00009921884],"domain_scores_gemma":[0.9986432,0.0006283906,0.0002198711,0.00008276989,0.0003284028,0.00009731382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001265235,0.0009755149,0.2587068,0.0001806584,0.0008274589,0.0002253974,0.00006665118,0.3847192,0.005345195,0.0006197983,0.002912339,0.3441557],"study_design_scores_gemma":[0.00002300932,0.0002211195,0.01287771,0.00001786507,0.00009949098,0.00004870144,0.00001096663,0.9841595,0.001396491,0.000896991,0.000235313,0.00001292045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8976697,0.002206439,0.09592888,0.0007783392,0.00005129108,0.00009411139,0.001076158,0.0006210819,0.001573918],"genre_scores_gemma":[0.9887727,0.0002052897,0.009552105,0.00007807987,0.00002313967,0.00004792946,0.0008956346,0.00001067427,0.0004146013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00517218,"threshold_uncertainty_score":0.01028419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01688947971787229,"score_gpt":0.3380750679108506,"score_spread":0.3211855881929783,"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."}}