{"id":"W1971698939","doi":"10.1016/j.orhc.2013.12.003","title":"A simulation model for perioperative process improvement","year":2014,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; University of New Brunswick","funders":"","keywords":"Overtime; Perioperative; Operations management; Schedule; Scheduling (production processes); Medicine; Revenue; Surgical procedures; Operations research; Medical emergency; Computer science; Surgery; Engineering; Business","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":[0.004194641,0.0002030304,0.0003539673,0.0003108857,0.009714887,0.0001147836,0.000220256,0.0002462413,0.00005753124],"category_scores_gemma":[0.003746795,0.0001838863,0.00008546648,0.0004448399,0.00007037987,0.0003714208,0.00004988888,0.0006069646,0.00003156225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047034,"about_ca_system_score_gemma":0.005365281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000312235,"about_ca_topic_score_gemma":0.004728294,"domain_scores_codex":[0.9959777,0.0008120698,0.0009442343,0.0006145911,0.000526356,0.001125067],"domain_scores_gemma":[0.9900981,0.001238284,0.0000839865,0.0004649558,0.007727495,0.0003872319],"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":[0.0001023537,0.00005057238,0.00004790647,0.001017501,0.000009051737,2.185524e-8,0.03414533,0.9391481,0.00006308255,0.01659035,0.001228466,0.00759728],"study_design_scores_gemma":[0.001475593,0.0009465793,0.00002115847,0.0001354358,0.000005130182,6.588184e-8,0.01186025,0.9661746,0.00002138024,0.0001966441,0.01899251,0.0001706138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01687707,0.0001317317,0.9463406,0.02160327,0.0002842738,0.01346464,0.0005612054,0.0001089355,0.0006282642],"genre_scores_gemma":[0.9023089,0.00003441717,0.07150548,0.003944574,0.0006037055,0.01652182,0.001579263,0.00007535628,0.003426456],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8854319,"threshold_uncertainty_score":0.9915743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.246544303611805,"score_gpt":0.6188538996496851,"score_spread":0.3723095960378801,"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."}}