{"id":"W2783982473","doi":"10.1016/j.acap.2017.12.012","title":"Predicting Low-Resource-Intensity Emergency Department Visits in Children","year":2018,"lang":"en","type":"article","venue":"Academic Pediatrics","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation; Alberta Children's Hospital; University of Calgary","funders":"National Center for Advancing Translational Sciences; National Institutes of Health","keywords":"Emergency department; Medical emergency; Intensity (physics); Resource (disambiguation); Medicine; Emergency medicine; Computer science; Nursing; Computer network","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":[0.001135435,0.0005070891,0.0005194821,0.001344672,0.000677891,0.001199968,0.0006983861,0.0008513835,0.002144141],"category_scores_gemma":[0.009227731,0.0003498693,0.0009259652,0.00172109,0.0004479858,0.001077122,0.001051136,0.001616188,0.0002396291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076209,"about_ca_system_score_gemma":0.001505542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01748759,"about_ca_topic_score_gemma":0.02638058,"domain_scores_codex":[0.9988226,0.0003313358,0.0001409989,0.0001027922,0.0001882537,0.0004140247],"domain_scores_gemma":[0.9962946,0.001154596,0.001399605,0.0001211646,0.0003014166,0.0007286345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000170065,0.00001946607,0.9993942,0.00000727104,0.00001445483,0.00002863912,0.00003990367,0.00001791241,0.00001279532,0.00001785759,0.0000758121,0.0003548159],"study_design_scores_gemma":[0.000002061179,0.00003009692,0.9988939,0.00002164061,0.00001580485,0.0001105271,0.0006340838,0.0001032022,0.00003160722,0.00002523153,0.0001295072,0.000002390793],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978027,0.00052781,0.00005473793,0.0002143909,0.00001683333,0.00001009479,0.0004042821,0.000002420417,0.0009667174],"genre_scores_gemma":[0.9989766,0.0002676343,0.0001354009,0.00004430184,0.00002247945,0.000008900272,0.0004379721,0.000002577643,0.0001040679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01748759,"threshold_uncertainty_score":0.03477162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234443973946716,"score_gpt":0.2867177155051396,"score_spread":0.2743732757656725,"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."}}