{"id":"W2008099582","doi":"10.1097/pec.0b013e31802c611e","title":"Computer Modeling of Patient Flow in a Pediatric Emergency Department Using Discrete Event Simulation","year":2007,"lang":"en","type":"article","venue":"Pediatric Emergency Care","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Children's Hospital","funders":"","keywords":"Overcrowding; Staffing; Emergency department; Medicine; Triage; Discrete event simulation; Scheduling (production processes); Medical emergency; Emergency medicine; Simulation; Operations management; Computer science; Nursing; Engineering","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":[0.0007695645,0.0002433127,0.0003316668,0.0005149149,0.0004821995,0.00000311742,0.0001288768,0.0002550923,0.0003699269],"category_scores_gemma":[0.000136106,0.000240163,0.000135246,0.00120487,0.000004952307,0.0001889057,0.00009785138,0.0003940546,0.00001810057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003889844,"about_ca_system_score_gemma":0.0003815272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005137133,"about_ca_topic_score_gemma":0.0008133685,"domain_scores_codex":[0.9959741,0.0003254096,0.002151666,0.0004240591,0.0004826536,0.000642124],"domain_scores_gemma":[0.9980947,0.00008904182,0.0004545031,0.0003171843,0.0008539707,0.0001905421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001946738,0.00004693644,0.3637494,0.0003494413,0.000003560931,0.000001786575,0.005470027,0.6296604,0.000001536852,0.00001909535,0.00003830738,0.0006400755],"study_design_scores_gemma":[0.00037364,0.0001002045,0.02030173,0.00002162943,0.00007132847,1.905798e-7,0.001694858,0.9771035,0.000001382437,0.00004441754,0.00003979722,0.0002472911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7104823,0.001927291,0.2841484,0.00003159795,0.002217332,0.0009662354,0.00003717085,0.00003751308,0.0001521681],"genre_scores_gemma":[0.9777555,0.002272669,0.01821104,0.00002402234,0.001434947,0.00005048663,0.0001942186,0.0000418238,0.00001525942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3474431,"threshold_uncertainty_score":0.9793563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04960313274152152,"score_gpt":0.4101688805266198,"score_spread":0.3605657477850983,"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."}}