{"id":"W2064772785","doi":"10.5539/mas.v6n11p9","title":"Evaluating Emergency Department Resource Capacity Using Simulation","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Sains Malaysia; Universiti Utara Malaysia","keywords":"Triage; Emergency department; Surge Capacity; Resource (disambiguation); Resource allocation; Government (linguistics); Computer science; Resource planning; Operations management; Medical emergency; Business; Medicine; Environmental resource management; Nursing; Coronavirus disease 2019 (COVID-19); Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001426214,0.0008325367,0.0006990483,0.0009218657,0.0004670473,0.0009914166,0.0008899096,0.0009643266,0.001500949],"category_scores_gemma":[0.004884251,0.0004363011,0.0006076194,0.001096328,0.0005183749,0.0009576214,0.0006338004,0.0006483346,0.0001197056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002491224,"about_ca_system_score_gemma":0.001854305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02953298,"about_ca_topic_score_gemma":0.01397718,"domain_scores_codex":[0.9990096,0.0005530403,0.00005263601,0.0000839905,0.0001152978,0.0001854349],"domain_scores_gemma":[0.9943592,0.004174386,0.0004149733,0.000238604,0.0005417779,0.0002710201],"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":[0.0001018077,0.00008874555,0.00281754,0.00002031401,0.00002113711,0.00002571139,0.0000228488,0.9946836,0.0003013079,0.000493281,0.0001324185,0.001291329],"study_design_scores_gemma":[0.00002485915,0.0001289512,0.001180979,0.000007103145,0.00001086496,0.000009344881,0.0000639516,0.9975777,0.0004743914,0.0003398239,0.000173015,0.000009090842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982521,0.0001297344,0.01087196,0.0002073212,0.00002746694,0.000143617,0.0005534216,0.0001328842,0.005412641],"genre_scores_gemma":[0.9955526,0.00007916628,0.003676439,0.00001715219,0.000003456364,0.00006226135,0.0002448032,0.000007306356,0.0003567485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02953298,"threshold_uncertainty_score":0.05872214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.372193345429102,"score_gpt":0.5291388308163495,"score_spread":0.1569454853872475,"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."}}