{"id":"W3042407815","doi":"10.2196/19892","title":"Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; National Science Foundation","keywords":"Decompensation; Heart failure; Medicine; Intensive care unit; Myocardial infarction; Intensive care medicine; Risk factor; Logistic regression; Emergency medicine; Cardiology; Heart disease; Internal medicine; Vital signs; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001388913,0.0005474766,0.0003877808,0.001395814,0.0002925634,0.0006640209,0.0004410902,0.0004235307,0.001156098],"category_scores_gemma":[0.006906889,0.0001529285,0.0003198314,0.0007848514,0.0002188843,0.0005956659,0.0006146063,0.0008268467,0.0001813339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003296567,"about_ca_system_score_gemma":0.0007089806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003605495,"about_ca_topic_score_gemma":0.005935443,"domain_scores_codex":[0.99941,0.000235114,0.00006604649,0.0001121661,0.0000964307,0.00008034482],"domain_scores_gemma":[0.9949421,0.002071469,0.001801983,0.0001292297,0.0004207425,0.0006344597],"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":[0.00009914987,0.00006547641,0.994961,0.00001703291,0.00002012004,0.00004581092,0.0000238608,0.0007113622,0.0001833697,0.00001369017,0.0001808218,0.003678158],"study_design_scores_gemma":[0.00001927087,0.0002521699,0.9694889,0.00003253853,0.00002666915,0.0002395052,0.0001754033,0.02904601,0.0003445738,0.0001963523,0.0001680198,0.00001057597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974704,0.0002044889,0.001345532,0.0001986306,0.00001372878,0.00004107648,0.0003913256,0.00002261616,0.0003122338],"genre_scores_gemma":[0.9981474,0.00006068462,0.001287266,0.00002080229,0.00002206258,0.00001428347,0.0004175141,0.000001816176,0.00002828893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003605495,"threshold_uncertainty_score":0.007345319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05887880791557606,"score_gpt":0.3727713185122544,"score_spread":0.3138925105966783,"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."}}