{"id":"W3006920821","doi":"10.23919/cinc49843.2019.9005923","title":"Development of an Early Warning System for Sepsis","year":2019,"lang":"en","type":"article","venue":"Computing in Cardiology","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Undersampling; Sepsis; Random forest; Medicine; Ranking (information retrieval); Statistics; Computer science; Emergency medicine; Internal medicine; Artificial intelligence; Intensive care medicine; 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":[0.0003326444,0.00008881208,0.0005042011,0.00009166078,0.00002672717,0.000002888685,0.00005372267,0.00007862259,0.000002604646],"category_scores_gemma":[0.00002869556,0.00007588277,0.00008650249,0.00007028958,0.00001384352,0.00001274446,0.00003491676,0.00005938346,0.00001434878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001300608,"about_ca_system_score_gemma":0.00006215924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001330594,"about_ca_topic_score_gemma":0.0000018261,"domain_scores_codex":[0.9991731,0.00004863516,0.0002935063,0.0002265427,0.00006843905,0.0001897506],"domain_scores_gemma":[0.9994347,0.0001981658,0.00008165641,0.0001854495,0.00005892408,0.00004107646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005081942,0.00005240436,0.9804826,0.0002261734,0.0001699451,0.00001686431,0.0009345744,0.001236653,0.001124691,0.0001548617,0.00002143241,0.01552894],"study_design_scores_gemma":[0.002194578,0.0004194377,0.9880642,0.0003414346,0.00005227481,0.00003905851,0.0005922385,0.003362739,0.003846851,0.000004297884,0.0009868796,0.00009595537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971223,0.0001399302,0.001003946,0.00002215741,0.0003284829,0.0004320535,0.000001253523,0.00003456402,0.0009153335],"genre_scores_gemma":[0.9797994,0.00000104621,0.02002157,0.00003158142,0.00007949799,0.00002126905,0.00001919144,0.00001246471,0.0000140189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01901762,"threshold_uncertainty_score":0.309441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0709238958552701,"score_gpt":0.3418817390065755,"score_spread":0.2709578431513054,"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."}}