{"id":"W4282827048","doi":"10.1007/s43678-022-00318-9","title":"The waiting game: managing flow by applying queuing theory in Canadian emergency departments","year":2022,"lang":"en","type":"letter","venue":"Canadian Journal of Emergency Medicine","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Queueing theory; Medicine; Game theory; Medical emergency; Operations research; Operations management; Computer science; Mathematical economics; Computer network; Engineering; Economics","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006312497,0.0003979537,0.0006528916,0.001160247,0.003727365,0.00001422348,0.0007870262,0.0005246311,0.02031484],"category_scores_gemma":[0.002264852,0.0003153846,0.0001426875,0.0009633026,0.00007158057,0.0001776806,0.00003260371,0.006038378,0.00002116303],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002829572,"about_ca_system_score_gemma":0.009247118,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6593311,"about_ca_topic_score_gemma":0.9354814,"domain_scores_codex":[0.9922078,0.002111378,0.002803966,0.0003608961,0.000632066,0.00188394],"domain_scores_gemma":[0.9960461,0.0004204246,0.001151836,0.0004492146,0.0006280733,0.001304388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008288216,0.000003054845,0.006445433,0.0001695737,0.00007226784,0.000348872,0.003672033,0.0008795241,0.000001698595,0.0002265014,0.9860415,0.002131295],"study_design_scores_gemma":[0.0003345754,0.000102003,0.00011892,0.0005355569,0.00007004685,0.000008582024,0.005269207,0.003164487,9.208434e-8,0.0006536058,0.9894654,0.0002775563],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002208151,0.02032829,0.0006424431,0.9103974,0.0485322,0.001843418,0.0002276504,0.00001910717,0.01580141],"genre_scores_gemma":[0.1251192,0.05610665,0.001086153,0.6140474,0.07311318,0.002283073,0.005689539,0.001281392,0.1212734],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.2963499,"threshold_uncertainty_score":0.9999298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005790058519941,"score_gpt":0.4124332619463413,"score_spread":0.3118542560943472,"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."}}