{"id":"W4366084120","doi":"10.1007/s00259-023-06218-z","title":"Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging","year":2023,"lang":"en","type":"article","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Calgary","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; British Heart Foundation","keywords":"Medicine; Myocardial perfusion imaging; Coronary artery disease; Internal medicine; Hazard ratio; Cardiology; Cohort; Population; Radiology; Confidence interval","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":[0.001191791,0.000366797,0.000480745,0.001067879,0.0002831762,0.0005008202,0.0004492587,0.0003849818,0.0008171709],"category_scores_gemma":[0.005873936,0.0001372339,0.0004904012,0.0005285567,0.000360083,0.0003316999,0.0005346246,0.0005331704,0.0002657221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000357458,"about_ca_system_score_gemma":0.0006155478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002308089,"about_ca_topic_score_gemma":0.002958572,"domain_scores_codex":[0.9993269,0.0002741888,0.00005505318,0.0001714568,0.00009745775,0.00007497992],"domain_scores_gemma":[0.9971233,0.001302475,0.0008027356,0.0003360711,0.0002674177,0.0001680148],"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":[0.0004207738,0.0001640916,0.9720088,0.0000263925,0.000197052,0.00006779427,0.00006689819,0.003077694,0.0009845677,0.0001333186,0.0007852177,0.02206734],"study_design_scores_gemma":[0.00008696617,0.0004353732,0.8999469,0.00003621108,0.0001615817,0.0007068827,0.0001767985,0.09360418,0.001432072,0.002399343,0.0009816869,0.00003202772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912534,0.0003216476,0.006881951,0.0002324393,0.00001552666,0.00004904028,0.0005379088,0.00006553221,0.0006424705],"genre_scores_gemma":[0.9960403,0.00006182501,0.002822011,0.00004398385,0.00002167658,0.00002544685,0.0008486206,0.000009147707,0.0001269116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002308089,"threshold_uncertainty_score":0.006302893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008715423093344594,"score_gpt":0.2221128172480975,"score_spread":0.2133973941547529,"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."}}