{"id":"W3201828575","doi":"10.1016/j.cardfail.2021.08.003","title":"Risk Prediction in Cardiogenic Shock: Current State of Knowledge, Challenges and Opportunities","year":2021,"lang":"en","type":"review","venue":"Journal of Cardiac Failure","topic":"Mechanical Circulatory Support Devices","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; University Health Network","funders":"","keywords":"Medicine; Cardiogenic shock; Intensive care medicine; Variety (cybernetics); Risk analysis (engineering); Psychological intervention; Shock (circulatory); Cardiology; Internal medicine; Artificial intelligence; Myocardial infarction; Computer science","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":[0.002585887,0.001086668,0.003138207,0.001748179,0.00029888,0.002291108,0.001275235,0.001579168,0.002529227],"category_scores_gemma":[0.005898127,0.0003319349,0.001289864,0.001499738,0.0005566475,0.001546011,0.0009518847,0.002746261,0.0007423791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004811757,"about_ca_system_score_gemma":0.001874816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00153816,"about_ca_topic_score_gemma":0.001919979,"domain_scores_codex":[0.9992888,0.0001812232,0.0001397939,0.0001401971,0.0002050473,0.00004482857],"domain_scores_gemma":[0.9952472,0.003513081,0.0004044385,0.00005589617,0.0006491714,0.0001302363],"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":[0.000201448,0.0001364828,0.002603293,0.01645673,0.0004057086,0.00008963037,0.00007325876,0.0005770376,0.0002757439,0.002369622,0.01188646,0.9649246],"study_design_scores_gemma":[0.0002814328,0.00121918,0.0232516,0.09126315,0.00567676,0.003525189,0.000893726,0.004293058,0.001341862,0.02737324,0.8405426,0.0003383131],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001970135,0.9985464,0.0002412527,0.0005939386,0.0001407168,0.000003086832,0.00002279603,0.000005449203,0.0002493155],"genre_scores_gemma":[0.002258484,0.9963126,0.0004596983,0.0002923835,0.0005276321,0.000007746922,0.00003846379,0.000001647832,0.0001014116],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003138207,"threshold_uncertainty_score":0.01367563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05378664898156323,"score_gpt":0.2844228377953325,"score_spread":0.2306361888137693,"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."}}