{"id":"W2971361581","doi":"10.1097/rct.0000000000000917","title":"Prototype Ultrahigh-Resolution Computed Tomography for Chest Imaging: Initial Human Experience","year":2019,"lang":"en","type":"article","venue":"Journal of Computer Assisted Tomography","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto","funders":"Canon Medical Systems Corporation","keywords":"Medicine; Image quality; Nuclear medicine; Image resolution; Tomography; Image noise; Iterative reconstruction; Scanner; Confidence interval; Radiology; Optics; Image (mathematics); Artificial intelligence; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004329119,0.0009906518,0.0004720706,0.0003608304,0.0002769595,0.0007397894,0.001096543,0.001084348,0.00389094],"category_scores_gemma":[0.007734898,0.0002941674,0.0004360063,0.0002095536,0.0007704007,0.0009781707,0.0008024551,0.0005921363,0.001037146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003446958,"about_ca_system_score_gemma":0.0003348585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002961682,"about_ca_topic_score_gemma":0.0004452411,"domain_scores_codex":[0.9980429,0.001076027,0.0001355316,0.0002547673,0.0003401132,0.0001505727],"domain_scores_gemma":[0.9974074,0.001231321,0.0001493166,0.0004284306,0.000321248,0.0004622708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0158439,0.06463154,0.1424074,0.001681432,0.0004684836,0.01245846,0.008279087,0.009151005,0.2086845,0.001213566,0.005220469,0.5299602],"study_design_scores_gemma":[0.004692643,0.4646276,0.2818217,0.0003497034,0.0005218373,0.1025021,0.003516946,0.02810095,0.07763367,0.001464904,0.03429361,0.0004743365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914038,0.0006369484,0.006707177,0.00009611432,0.00002716961,0.0002095417,0.00004832913,0.00006318323,0.000807756],"genre_scores_gemma":[0.9747881,0.0008935007,0.02245816,0.0002180906,0.00007751464,0.0001683756,0.0003140077,0.00004816354,0.001034092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004329119,"threshold_uncertainty_score":0.0228948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667530537929494,"score_gpt":0.3314837592393499,"score_spread":0.3048084538600549,"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."}}