{"id":"W2972623710","doi":"10.1097/pcc.0000000000002121","title":"A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting*","year":2019,"lang":"en","type":"article","venue":"Pediatric Critical Care Medicine","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Wellcome Trust","keywords":"Medicine; Triage; Receiver operating characteristic; Psychological intervention; Referral; Emergency medicine; Mortality rate; Vital signs; Mechanical ventilation; Pediatrics; Acute care; Internal medicine; Surgery; Health care","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":[0.0003526232,0.0002810954,0.0006354443,0.0003494873,0.00007216457,0.00001453042,0.00006144832,0.0001384403,0.0003098488],"category_scores_gemma":[0.001456767,0.0001901886,0.0001308184,0.0005271697,0.00007897663,0.00004567124,0.00002203643,0.0004045239,0.00002826706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001962742,"about_ca_system_score_gemma":0.0001393611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002379542,"about_ca_topic_score_gemma":0.00003798066,"domain_scores_codex":[0.9980519,0.0000881349,0.0004143423,0.0005256267,0.0004777699,0.000442217],"domain_scores_gemma":[0.998083,0.001121173,0.00007201517,0.0003088454,0.0002044943,0.0002104721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008958438,0.0004109645,0.9952227,0.0005402373,0.00006122996,0.00008722793,0.0002568519,0.0000803505,0.00001009257,0.00006942255,0.000565482,0.001799523],"study_design_scores_gemma":[0.03020097,0.01174699,0.950784,0.0004312188,0.002645781,0.0001234458,0.0001731228,0.001593333,0.0002018771,0.00003108106,0.001691707,0.0003764533],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884038,0.002104261,0.0005143174,0.006315073,0.0001267123,0.001959815,0.00004091283,0.0001257901,0.0004093203],"genre_scores_gemma":[0.996294,0.0001276781,0.0004441976,0.001257859,0.000816142,0.0003006308,0.0006196189,0.00005260548,0.0000872547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04443875,"threshold_uncertainty_score":0.7755667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01662776058028962,"score_gpt":0.32213720369861,"score_spread":0.3055094431183203,"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."}}