{"id":"W2964110549","doi":"10.1097/tp.0000000000002771","title":"Lung Density Analysis Using Quantitative Chest CT for Early Prediction of Chronic Lung Allograft Dysfunction","year":2019,"lang":"en","type":"article","venue":"Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Lung; Lung transplantation; Receiver operating characteristic; Radiology; Retrospective cohort study; Cohort; Lung volumes; Nuclear medicine; Surgery; Internal medicine","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":[0.0003721247,0.0001661234,0.0004479674,0.0003718852,0.00007799544,0.00001344952,0.00004419274,0.00008105717,0.0000624446],"category_scores_gemma":[0.0000124709,0.0001557142,0.0002914102,0.0005453206,0.00003856827,0.000258242,0.000001903249,0.0001105285,0.000003385155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299581,"about_ca_system_score_gemma":0.00008534255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003234079,"about_ca_topic_score_gemma":0.0001527156,"domain_scores_codex":[0.9987078,0.00008659306,0.0004335998,0.0003061129,0.0002698202,0.0001960484],"domain_scores_gemma":[0.9991338,0.0002516343,0.0001931207,0.0001714148,0.0001890427,0.00006098224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002049773,0.000100202,0.8654023,0.00277944,0.001496283,0.000009699654,0.002086977,0.003988489,0.1205498,0.0006796073,0.000004845861,0.0008524766],"study_design_scores_gemma":[0.003231571,0.0003702256,0.8360411,0.0001833119,0.007730702,0.00002949891,0.0001300349,0.1303591,0.02175481,0.00003724543,0.000009544712,0.000122842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5753425,0.00009708414,0.4235496,0.00002598463,0.0001732446,0.0005839239,0.0001357252,0.00003911895,0.00005280508],"genre_scores_gemma":[0.9723139,0.0001834778,0.02631407,0.00002470479,0.0000645347,0.00002086222,0.0009719646,0.00002125566,0.00008525101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3972355,"threshold_uncertainty_score":0.634984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03391115765971717,"score_gpt":0.3319094128620646,"score_spread":0.2979982552023475,"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."}}