{"id":"W3119321469","doi":"10.31083/j.rcm.2020.04.236","title":"Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence","year":2020,"lang":"en","type":"review","venue":"Reviews in Cardiovascular Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Biobank; Coronavirus disease 2019 (COVID-19); Medicine; Artificial intelligence; Risk assessment; Perspective (graphical); Applications of artificial intelligence; 2019-20 coronavirus outbreak; Data science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Disease; Computer science; Pathology; Bioinformatics; Infectious disease (medical specialty); Computer security","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":[0.001963191,0.0009483182,0.00211503,0.002944755,0.0002491952,0.001676316,0.001104978,0.001514954,0.00399033],"category_scores_gemma":[0.004485492,0.0003260464,0.001758196,0.001763006,0.000477228,0.001292062,0.0009867906,0.002379707,0.001630882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009897853,"about_ca_system_score_gemma":0.001757642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001895233,"about_ca_topic_score_gemma":0.002743936,"domain_scores_codex":[0.9992089,0.0002633915,0.0001512285,0.0001095749,0.0002276861,0.00003929085],"domain_scores_gemma":[0.9974941,0.001820598,0.0002113937,0.00005217428,0.0003597177,0.00006200885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007279214,0.00004900512,0.0004030065,0.03932976,0.0006056622,0.00007733121,0.00005489381,0.0004273863,0.0002411339,0.004545364,0.01951312,0.9346805],"study_design_scores_gemma":[0.00007407443,0.0002244273,0.003402584,0.06159104,0.001644784,0.001296334,0.000137027,0.0006939991,0.0005690748,0.0105612,0.9197217,0.00008375614],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000534656,0.9984995,0.0001831079,0.0005268807,0.0001484473,0.000008090046,0.00002393466,0.000007386859,0.0005492346],"genre_scores_gemma":[0.0008901293,0.9979593,0.0003843591,0.0003527402,0.0002023751,0.0000148805,0.00004710951,0.000002080616,0.0001470627],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00399033,"threshold_uncertainty_score":0.01334894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2103521703257797,"score_gpt":0.4451159245602222,"score_spread":0.2347637542344425,"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."}}