{"id":"W2960139033","doi":"10.33788/rcis.65.6","title":"Decision Support for Resource Optimization Using Discrete Event Simulation in Rehabilitation Hospitals","year":2019,"lang":"en","type":"article","venue":"Revista de Cercetare si Interventie Sociala","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discrete event simulation; Rehabilitation; Event (particle physics); Resource (disambiguation); Decision support system; Computer science; Psychology; Medicine; Simulation; Artificial intelligence; Physical therapy; Physics","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":[0.002829941,0.0008231296,0.00129914,0.0006805774,0.0006449813,0.002210913,0.001242734,0.001444366,0.003833235],"category_scores_gemma":[0.008573788,0.0006992142,0.0009442389,0.0005923621,0.0006594474,0.0008265033,0.001307826,0.001553244,0.0003046174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288912,"about_ca_system_score_gemma":0.002469371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01369323,"about_ca_topic_score_gemma":0.00729813,"domain_scores_codex":[0.9981828,0.001281147,0.0001089007,0.0001343845,0.0001870518,0.0001056573],"domain_scores_gemma":[0.9901075,0.008644341,0.0003886466,0.0001749859,0.0004290535,0.0002553673],"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":[0.00009179246,0.00005806026,0.0004579096,0.00003610129,0.00002550967,0.00003906295,0.00004558584,0.9902037,0.0001830879,0.003601492,0.0001995297,0.00505814],"study_design_scores_gemma":[0.00001900735,0.00001239131,0.00003426087,0.000006283358,0.000003447113,0.000002591334,0.00001180101,0.9981505,0.0001050806,0.001425422,0.0002257186,0.000003521537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0691942,0.0003164026,0.9208484,0.001101152,0.0001293966,0.0003024994,0.0002809849,0.001153579,0.006673367],"genre_scores_gemma":[0.8274662,0.0004228587,0.1689623,0.0001670705,0.0000542037,0.0004807594,0.0002416805,0.00006636434,0.002138588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01369323,"threshold_uncertainty_score":0.02722704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04210415436918617,"score_gpt":0.4490524703168881,"score_spread":0.4069483159477019,"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."}}