{"id":"W3048811876","doi":"10.1515/dx-2020-0030","title":"Identifying children at high risk for infection-related decompensation using a predictive emergency department-based electronic assessment tool","year":2020,"lang":"en","type":"article","venue":"Diagnosis","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Children's Health Foundation; U.S. Department of Education","keywords":"Emergency department; Decompensation; Medicine; Predictive value; Sepsis; Emergency medicine; Gold standard (test); Incidence (geometry); Positive predicative value; Pediatrics; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.003150599,0.0006550839,0.0003463279,0.003944031,0.0002346702,0.001182382,0.0007090613,0.0003409986,0.001547233],"category_scores_gemma":[0.01907313,0.0001955581,0.0006677932,0.002176293,0.0001909761,0.0009121552,0.001097399,0.0006692264,0.0003182479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005403177,"about_ca_system_score_gemma":0.001534388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140198,"about_ca_topic_score_gemma":0.003407217,"domain_scores_codex":[0.9976934,0.0007957835,0.0004182972,0.0002938284,0.0006422542,0.0001565069],"domain_scores_gemma":[0.9906945,0.004345468,0.00257099,0.0003384425,0.001561328,0.0004891965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001533586,0.000213348,0.9003503,0.0001346036,0.00009112751,0.000115514,0.0001350758,0.001911062,0.0004524403,0.0002392616,0.002826432,0.09337747],"study_design_scores_gemma":[0.0001146842,0.0008451743,0.9343134,0.0004785818,0.00024026,0.0009637444,0.0005955144,0.05218434,0.004241137,0.0007985681,0.005148359,0.0000762506],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9571829,0.0007490031,0.02676765,0.001735486,0.0001449189,0.0008763291,0.004581974,0.001105222,0.006856548],"genre_scores_gemma":[0.9380615,0.0003057311,0.05801025,0.0003085009,0.00006335568,0.0003496944,0.002390908,0.00002714882,0.0004827195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003944031,"threshold_uncertainty_score":0.01666212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05240763782104155,"score_gpt":0.3519507629669755,"score_spread":0.2995431251459339,"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."}}