{"id":"W6892142063","doi":"10.5167/uzh-208809","title":"Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry","year":2021,"lang":"en","type":"article","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariable calculus; Myocardial perfusion imaging; Medical imaging; Prognostic variable; Perfusion scanning; Set (abstract data type); Risk stratification; Computed tomography","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":[0.005868912,0.0008597305,0.001179872,0.0006686322,0.0002659071,0.0009134515,0.0007827376,0.0003883812,0.0005177181],"category_scores_gemma":[0.01990807,0.0002715507,0.001037592,0.0005180615,0.0002758984,0.0007220859,0.0006876169,0.0007108983,0.0002063625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005891602,"about_ca_system_score_gemma":0.001057413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007330658,"about_ca_topic_score_gemma":0.008161817,"domain_scores_codex":[0.99804,0.001042102,0.0001695791,0.0003462899,0.0003047267,0.00009737226],"domain_scores_gemma":[0.9930736,0.004548508,0.0008581422,0.0007157755,0.000625209,0.0001787513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002092847,0.0002854344,0.8968839,0.00008748851,0.0003173464,0.00009830877,0.00006974194,0.02811245,0.002016752,0.0001354785,0.00154014,0.06836023],"study_design_scores_gemma":[0.0004679062,0.002541631,0.6218214,0.00006903733,0.0007065635,0.0004125639,0.0002092743,0.3619682,0.009316185,0.0009876697,0.001438514,0.0000610984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876958,0.0003631529,0.009398161,0.0002704329,0.000008817224,0.00004969222,0.00170811,0.0002205467,0.0002852467],"genre_scores_gemma":[0.9892541,0.0001079413,0.006814112,0.0000555741,0.0000173313,0.00005576368,0.003591699,0.00002918634,0.00007428711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007330658,"threshold_uncertainty_score":0.03103817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01665894540462003,"score_gpt":0.225622251312018,"score_spread":0.208963305907398,"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."}}