{"id":"W3015116865","doi":"","title":"Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.","year":2020,"lang":"en","type":"preprint","venue":"PubMed","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Ground truth; Pearson product-moment correlation coefficient; Medicine; Correlation; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Radiology; Nuclear medicine; Ground-glass opacity; Correlation coefficient; Lung; Artificial intelligence; Computer science; Mathematics; Pathology; Disease; Statistics; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001032353,0.000746201,0.0005326363,0.003158515,0.000178724,0.001084028,0.0007737229,0.0008609975,0.0009032377],"category_scores_gemma":[0.003540578,0.0004033427,0.0005219039,0.0008081814,0.000237188,0.000531942,0.0007567288,0.0004307991,0.000632635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004037008,"about_ca_system_score_gemma":0.0005320416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002870943,"about_ca_topic_score_gemma":0.006565269,"domain_scores_codex":[0.9993655,0.00012334,0.00005826745,0.000236697,0.0001563988,0.00005977313],"domain_scores_gemma":[0.9986291,0.0003466623,0.0004820102,0.0001381014,0.000311961,0.00009215149],"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":[0.0007484662,0.0002047805,0.5590951,0.0005311703,0.0004592607,0.0006333631,0.0001566108,0.02070379,0.05328999,0.0004902395,0.006405556,0.3572817],"study_design_scores_gemma":[0.000100633,0.0003225079,0.4837358,0.0002200588,0.0002598497,0.005781278,0.0002572784,0.4610992,0.03755422,0.002114287,0.00846679,0.00008816832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6673463,0.005201547,0.3104343,0.0005734285,0.0001241808,0.0005670122,0.009191583,0.003655962,0.00290565],"genre_scores_gemma":[0.8647653,0.0007946501,0.1241033,0.0001313569,0.0001034114,0.0001602569,0.008400293,0.0001501921,0.001391297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003158515,"threshold_uncertainty_score":0.005708396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09756404912034772,"score_gpt":0.3238940410066454,"score_spread":0.2263299918862977,"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."}}