{"id":"W4283710255","doi":"10.2459/jcm.0000000000001329","title":"Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study","year":2022,"lang":"en","type":"article","venue":"Journal of Cardiovascular Medicine","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Surgical Specialties (Canada)","funders":"","keywords":"Medicine; Logistic regression; Confidence interval; Lasso (programming language); Coronavirus disease 2019 (COVID-19); Receiver operating characteristic; Covariate; Cohort; Machine learning; Emergency medicine; Internal medicine; Disease; Infectious disease (medical specialty)","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":[0.007222838,0.0009040052,0.0007640073,0.0007903667,0.0003258049,0.000911656,0.0006304137,0.0007449775,0.001293799],"category_scores_gemma":[0.01006943,0.000230588,0.001435667,0.0008385797,0.0003445013,0.0004315832,0.0006464026,0.0007108678,0.000541937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966952,"about_ca_system_score_gemma":0.0004770217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005154183,"about_ca_topic_score_gemma":0.003127505,"domain_scores_codex":[0.9979905,0.001320295,0.0000986798,0.000280256,0.0001411637,0.0001691698],"domain_scores_gemma":[0.993201,0.003038387,0.001403216,0.001155904,0.0005707978,0.0006307319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003908901,0.0005798809,0.9837723,0.00006878265,0.0006536464,0.0001939239,0.0002081008,0.001470071,0.0002058276,0.00006597701,0.00210595,0.006766743],"study_design_scores_gemma":[0.000276408,0.001058421,0.9897094,0.00003319695,0.0003052534,0.0001932457,0.0001441362,0.007333785,0.00008027424,0.00007511768,0.0007744306,0.00001633457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974505,0.0004469925,0.0002984726,0.0002892931,0.00002074837,0.00003228223,0.0009913285,0.00001307499,0.0004573551],"genre_scores_gemma":[0.9957373,0.000215236,0.0002759487,0.00009044785,0.00008435324,0.00005928322,0.003305818,0.00001350164,0.0002181102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007222838,"threshold_uncertainty_score":0.03819841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0504725121867231,"score_gpt":0.3964090187643568,"score_spread":0.3459365065776337,"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."}}