{"id":"W4308489971","doi":"10.1016/j.jacadv.2022.100126","title":"Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness","year":2022,"lang":"en","type":"article","venue":"JACC Advances","topic":"Mechanical Circulatory Support Devices","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ted Rogers Centre for Heart Research; University of Toronto","funders":"","keywords":"Cardiogenic shock; Intensive care medicine; Disease; Clinical trial; Critical illness; Medicine; Mechanism (biology); Computer science; Psychology; Critically ill; Pathology; Psychiatry; Epistemology","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":[0.0118172,0.001242497,0.001578815,0.004773142,0.0003479478,0.002689493,0.001339252,0.001368508,0.001532044],"category_scores_gemma":[0.02453081,0.0003412576,0.001527821,0.003060596,0.001299004,0.001633003,0.001300266,0.003468394,0.000616284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177604,"about_ca_system_score_gemma":0.001636611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002245995,"about_ca_topic_score_gemma":0.002686909,"domain_scores_codex":[0.9952305,0.003160812,0.0004196487,0.0004752649,0.0006166368,0.00009701993],"domain_scores_gemma":[0.9771497,0.01983757,0.001151958,0.0005068285,0.001139582,0.0002144484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002059724,0.0001927105,0.03302686,0.00407459,0.00143786,0.0001822891,0.0002243944,0.02361364,0.001286677,0.02619652,0.01246636,0.8970921],"study_design_scores_gemma":[0.000194179,0.001553155,0.07501715,0.01028692,0.001650835,0.002121375,0.0008610343,0.3288096,0.004100534,0.4186841,0.1562129,0.000508193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01553084,0.4487887,0.5040651,0.02119066,0.001848358,0.0004055531,0.001403471,0.000576438,0.006191021],"genre_scores_gemma":[0.2732863,0.3170813,0.3896557,0.006650294,0.007606993,0.001031001,0.001992217,0.0001716207,0.002524598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0118172,"threshold_uncertainty_score":0.06249607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443022257798559,"score_gpt":0.2455442654126828,"score_spread":0.2211140428346973,"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."}}