{"id":"W2891676555","doi":"10.1161/circheartfailure.118.005193","title":"Neural Networks for Prognostication of Patients With Heart Failure","year":2018,"lang":"en","type":"article","venue":"Circulation Heart Failure","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Ted Rogers Centre for Heart Research; University Health Network","funders":"","keywords":"Receiver operating characteristic; Medicine; Artificial neural network; Heart failure; Area under the curve; Discriminative model; Predictive modelling; Statistics; Cardiology; Artificial intelligence; Internal medicine; Computer science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001339366,0.00013691,0.0002896982,0.0001057558,0.0001218487,0.00001670529,0.00003167566,0.0001364632,0.0001110445],"category_scores_gemma":[0.0001088304,0.0001075704,0.0002077948,0.0002974474,0.000110359,0.0001201115,0.00001028436,0.0001099277,0.00001341183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003765414,"about_ca_system_score_gemma":0.00005000041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001133102,"about_ca_topic_score_gemma":0.00002069313,"domain_scores_codex":[0.9989385,0.0000391311,0.0002519125,0.0002671311,0.0003085498,0.0001947574],"domain_scores_gemma":[0.9986218,0.00005554662,0.00008545999,0.0003289506,0.0007934677,0.00011476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001892818,0.00008842888,0.9890605,0.0000561686,0.00007849771,9.532315e-8,0.00005901135,0.000690116,0.0002914621,0.00006276675,0.008704565,0.00071906],"study_design_scores_gemma":[0.001862895,0.0003988289,0.9120111,0.00005028739,0.0001709611,0.00001046969,0.00004777386,0.009061923,0.0003052575,0.000009299431,0.07594927,0.0001219289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621325,0.00003787127,0.03309358,0.002546889,0.0002910398,0.001750153,0.00001672228,0.0001003987,0.00003081472],"genre_scores_gemma":[0.9948689,5.288985e-7,0.003913098,0.0002721602,0.0004592992,0.00004601338,0.0003769481,0.00002959721,0.00003344825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07704945,"threshold_uncertainty_score":0.4386595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080035285895821,"score_gpt":0.2384016899638454,"score_spread":0.2276013371048872,"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."}}