{"id":"W2183373287","doi":"10.2310/8000.2012.120532","title":"Emergency physician workload modeling","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Workload; Strengths and weaknesses; Medicine; Emergency department; Compensation (psychology); Variety (cybernetics); Medical emergency; Emergency physician; Measure (data warehouse); Operations management; Computer science; Nursing; Psychology; Data mining; Artificial intelligence","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":[0.000758734,0.0006620952,0.0003782531,0.000728498,0.0004762355,0.00108374,0.001423473,0.0007744418,0.009523052],"category_scores_gemma":[0.004352543,0.0004440843,0.000819141,0.000969431,0.0001462924,0.0009456348,0.0005146634,0.0006334915,0.001719739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001337293,"about_ca_system_score_gemma":0.001887948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03735362,"about_ca_topic_score_gemma":0.02714352,"domain_scores_codex":[0.9993234,0.000194265,0.00003773628,0.0001807498,0.000127973,0.0001358771],"domain_scores_gemma":[0.9987803,0.0005079579,0.0001112084,0.000131762,0.0003565642,0.0001121657],"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":[0.0001230616,0.0002983752,0.01371631,0.00005466014,0.00004658172,0.0001516197,0.0001438378,0.929149,0.0008562206,0.008618179,0.009563353,0.0372787],"study_design_scores_gemma":[0.000008992172,0.00001629134,0.002018808,0.000005617941,0.000006441615,0.00002104113,0.00003362753,0.9936326,0.0002579194,0.001923985,0.002068505,0.000006214634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4884215,0.000375547,0.4137452,0.002646542,0.0004359568,0.0008756221,0.01231202,0.002574686,0.07861282],"genre_scores_gemma":[0.9263298,0.0002148521,0.04634206,0.0001930462,0.0001343084,0.0003671203,0.005230971,0.0001500124,0.02103796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03735362,"threshold_uncertainty_score":0.07427239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101059992868628,"score_gpt":0.3517758009819262,"score_spread":0.2507158081132982,"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."}}