{"id":"W4386910877","doi":"10.1177/08404704231199403","title":"Preparing your emergency department for disaster: Optimizing surge capacity during mass casualty events","year":2023,"lang":"en","type":"article","venue":"Healthcare Management Forum","topic":"Disaster Response and Management","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Medical emergency; Surge Capacity; Emergency department; Mass-casualty incident; Health care; Doors; Mass Casualty; Business; Medicine; Operations management; Nursing; Poison control; Suicide prevention; Coronavirus disease 2019 (COVID-19); Engineering; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001595594,0.0004518538,0.0005232753,0.0005308305,0.002019593,0.00002528698,0.0004630409,0.0001641111,0.0001844133],"category_scores_gemma":[0.00005677556,0.0004585537,0.0002776411,0.0006760171,0.00002207397,0.0002997773,0.0009979713,0.0003422574,0.000653719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006110662,"about_ca_system_score_gemma":0.00005801889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002913578,"about_ca_topic_score_gemma":0.001038762,"domain_scores_codex":[0.9943361,0.0004796999,0.001268339,0.0009370683,0.0006358621,0.002342938],"domain_scores_gemma":[0.9979885,0.0001134522,0.0004274406,0.0009505954,0.0001212742,0.0003987542],"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":[0.003883197,0.000918731,0.2933661,0.05820721,0.002522331,0.0003277792,0.03519776,0.002008236,0.0003057467,0.09584682,0.4657556,0.0416605],"study_design_scores_gemma":[0.007210842,0.0005103447,0.08789097,0.002096701,0.0003669518,0.000003973139,0.1141889,0.003523773,0.00008619353,0.01229763,0.7699119,0.001911779],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236373,0.0002202472,0.01219434,0.01188138,0.006996341,0.01456769,0.0003739884,0.001738645,0.02839009],"genre_scores_gemma":[0.9305961,0.0009428113,0.002456339,0.001259824,0.0003515168,0.007471678,0.0004073455,0.0001845946,0.0563298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3041563,"threshold_uncertainty_score":0.9997866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041212328269636,"score_gpt":0.4157491339674185,"score_spread":0.3116279011404548,"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."}}