{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009371635,0.0001178246,0.0001808594,0.00007480624,0.0004626196,0.00001808216,0.00009227434,0.00003615774,0.00225579],"category_scores_gemma":[0.0006794891,0.0001122886,0.00006466578,0.0004230988,0.0000688725,0.00009623572,0.00004652854,0.001262207,0.000003338173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003742267,"about_ca_system_score_gemma":0.0008632621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00305518,"about_ca_topic_score_gemma":0.00002870635,"domain_scores_codex":[0.9983394,0.0002948114,0.000219093,0.000313151,0.0005992252,0.0002343063],"domain_scores_gemma":[0.9993929,0.00005944175,0.0001704381,0.0001512351,0.0001345607,0.00009139013],"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":[0.0001616423,0.00001745984,0.1337502,0.00005493981,0.0001392621,0.00001064019,0.003177567,0.5020877,0.0005720949,0.00005170393,0.01260252,0.3473742],"study_design_scores_gemma":[0.001166872,0.0001434436,0.007639855,0.00009210859,0.0002958357,0.00002546808,0.0010043,0.9167918,0.00005407021,0.00007627507,0.07260399,0.0001059629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524589,0.01375449,0.02068427,0.001631813,0.003441715,0.0009410073,0.00004534808,0.0002783575,0.006764132],"genre_scores_gemma":[0.9953969,0.001785183,0.0007347584,0.0005971628,0.000318863,0.0004304288,0.0001842422,0.00002899874,0.0005234323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4147041,"threshold_uncertainty_score":0.9986563,"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."}}