{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001698249,0.0002749031,0.000392588,0.0001158239,0.0003440878,0.00003073475,0.0000590859,0.0001195071,0.0007945264],"category_scores_gemma":[0.000202583,0.0002597297,0.0003342053,0.0003532896,0.00002092881,0.000132623,0.00004092158,0.0001697618,0.00003957446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038882,"about_ca_system_score_gemma":0.0001571371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005687024,"about_ca_topic_score_gemma":0.0001240303,"domain_scores_codex":[0.998121,0.00009637605,0.0004880323,0.0005561854,0.0003175039,0.0004208999],"domain_scores_gemma":[0.9989229,0.0002370286,0.0002983482,0.0002274758,0.0001402152,0.0001740371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000121653,0.0007548834,0.992615,0.00004411462,0.001079558,0.000004232924,0.000107174,0.00191006,0.0001427177,0.0001524525,0.001137272,0.001930835],"study_design_scores_gemma":[0.004445846,0.001300806,0.9669971,0.00008007214,0.002089962,0.000004401179,0.00001306459,0.01534835,0.008998591,0.0002080024,0.000280675,0.0002330917],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896722,0.000522933,0.00571304,0.0009985667,0.0002482607,0.002471049,0.0001824771,0.0001641993,0.00002724],"genre_scores_gemma":[0.993267,0.001180625,0.001992851,0.0004051405,0.0001602432,0.001920201,0.001010596,0.00005555292,0.000007799877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02561791,"threshold_uncertainty_score":0.9999855,"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."}}