{"id":"W6958525947","doi":"10.6084/m9.figshare.21620877.v1","title":"Development and validation of echocardiography-based machine-learning models to predict mortality","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ejection fraction; Gradient boosting; Heart failure; Heart failure with preserved ejection fraction; Risk assessment; Boosting (machine learning); Risk of mortality","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":[0.00821843,0.001512798,0.0006198354,0.001230105,0.0003024023,0.00101634,0.001554516,0.0009649612,0.001017052],"category_scores_gemma":[0.01139895,0.0004374164,0.001124886,0.0004058136,0.0004208896,0.0006741844,0.001198479,0.001419744,0.0007117101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195641,"about_ca_system_score_gemma":0.001998992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01445662,"about_ca_topic_score_gemma":0.01170631,"domain_scores_codex":[0.9986626,0.0007099988,0.0001036482,0.0002540573,0.0001597556,0.000109855],"domain_scores_gemma":[0.9952095,0.002505087,0.0002886107,0.0004659648,0.001282677,0.0002480498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006683536,0.000795597,0.1522164,0.0001507894,0.001001546,0.0001813695,0.0001194094,0.7091534,0.003079556,0.0009547583,0.006961755,0.1247172],"study_design_scores_gemma":[0.00002908858,0.0001342397,0.007592739,0.00002939103,0.00005244351,0.0000244362,0.00001664403,0.9903622,0.0009687258,0.0004195287,0.0003571109,0.00001350854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8599801,0.0017321,0.1289274,0.001458829,0.0002494058,0.0002993837,0.002863317,0.00186968,0.002619751],"genre_scores_gemma":[0.9588146,0.0002127831,0.03569403,0.0002435182,0.00005647421,0.0001834501,0.003852312,0.00005494447,0.0008878246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01445662,"threshold_uncertainty_score":0.04346371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06476593529827417,"score_gpt":0.2595549325285655,"score_spread":0.1947889972302913,"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."}}