{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0040337,0.0001138924,0.0001275099,0.0001144582,0.003250598,0.0000183381,0.0001671517,0.00009418312,0.0001968288],"category_scores_gemma":[0.0002912214,0.0001047686,0.00002072954,0.0005910821,0.00008258572,0.0003705495,0.00009631337,0.0002704849,0.00008784683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004320238,"about_ca_system_score_gemma":0.0003841391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005904546,"about_ca_topic_score_gemma":0.00001391404,"domain_scores_codex":[0.9977102,0.000190917,0.0004752906,0.0003084828,0.0005997939,0.0007152582],"domain_scores_gemma":[0.9988862,0.0001038795,0.0001803218,0.0003240614,0.0002537617,0.0002517305],"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":[0.00001653794,0.00006930331,0.01619041,0.00004491974,0.000003073246,6.086432e-8,0.01315361,0.8525298,0.1065252,0.003955841,0.00002836591,0.007482885],"study_design_scores_gemma":[0.0001468671,0.00001075553,0.005066453,0.00001626961,0.000007456904,2.448886e-7,0.0004116237,0.9933013,0.0002217796,0.0003566095,0.0003414584,0.0001191888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5972838,0.00002474884,0.3976268,0.00007068398,0.0002863113,0.0005486552,0.000003095134,0.00005504486,0.004100917],"genre_scores_gemma":[0.942162,0.00000214581,0.05711891,0.0002516494,0.0002590095,0.00007312907,0.000009302459,0.00001458717,0.0001092842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3448782,"threshold_uncertainty_score":0.9980471,"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."}}