{"id":"W4252405773","doi":"10.1109/wsc.2017.8248022","title":"Data-driven generic discrete event simulation model of hospital patient flow considering surge","year":2017,"lang":"en","type":"article","venue":"2017 Winter Simulation Conference (WSC)","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Discrete event simulation; Computer science; Surge; Event (particle physics); Data modeling; Flow (mathematics); Simulation; Software engineering; Mechanics; Engineering; Physics; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005379688,0.0002429036,0.0004084428,0.0001180481,0.001497394,0.0001168571,0.0005367293,0.0002411554,0.0003334857],"category_scores_gemma":[0.001078321,0.0002273538,0.00007755623,0.00005642992,0.0001035118,0.00119281,0.0004603985,0.000350565,0.00006995094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001136998,"about_ca_system_score_gemma":0.0006118052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002038989,"about_ca_topic_score_gemma":0.0003582357,"domain_scores_codex":[0.9973369,0.0002843912,0.001059745,0.0005414608,0.0003749259,0.0004025512],"domain_scores_gemma":[0.995598,0.0003495159,0.001009485,0.00174545,0.001124036,0.0001735555],"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":[0.00003608306,0.00005046432,0.01159319,0.00006794329,0.00002686469,0.000001278956,0.003031002,0.9806653,0.00007897861,0.0002703582,0.0002084268,0.00397006],"study_design_scores_gemma":[0.0005782064,0.00009031695,0.004963467,0.000291637,0.0000288047,1.358743e-7,0.0003490948,0.9925311,0.0000206086,0.0001782091,0.0007246426,0.0002437713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.32904,0.00001720995,0.666423,0.0009918347,0.001094381,0.0009471005,0.0004645041,0.0000639292,0.0009580397],"genre_scores_gemma":[0.9825529,0.00002833101,0.0160296,0.0001487528,0.0001917973,0.00004341323,0.0006532682,0.00003437139,0.0003175976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6535129,"threshold_uncertainty_score":0.9998025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2764371507106436,"score_gpt":0.459977142235427,"score_spread":0.1835399915247834,"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."}}