{"id":"W4309328075","doi":"10.1089/hs.2022.0059","title":"Rethinking Surge Preparedness After COVID-19: Effective Patient Load Balancing Within Health Systems and Beyond","year":2022,"lang":"en","type":"article","venue":"Health Security","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Surge Capacity; Preparedness; Pandemic; Coronavirus disease 2019 (COVID-19); Healthcare system; Health care; Medical emergency; Medicine; Viral load; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Operations management; Political science; Infectious disease (medical specialty); Engineering; Family medicine; Disease; Human immunodeficiency virus (HIV)","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":[0.009910026,0.0002843682,0.0007122518,0.0001858218,0.007051416,0.00004753733,0.0001312807,0.0001815835,0.0002110992],"category_scores_gemma":[0.0007953796,0.0002864469,0.00005523551,0.0005419404,0.00006040844,0.0001986542,0.0003203588,0.001668435,0.00001078734],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.006249827,"about_ca_system_score_gemma":0.01188528,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02455365,"about_ca_topic_score_gemma":0.006446758,"domain_scores_codex":[0.9883869,0.00746712,0.001543314,0.0007758418,0.0008132824,0.0010136],"domain_scores_gemma":[0.996125,0.0008806263,0.0009442928,0.0005032488,0.0003242488,0.001222537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004986213,0.0002410014,0.04313243,0.007981902,0.00003577184,0.00004047385,0.9178917,0.01082483,0.000001554836,0.006718888,0.01113619,0.001496597],"study_design_scores_gemma":[0.009558772,0.006541177,0.01770706,0.003375732,0.00006766877,0.0003086697,0.389239,0.3011736,0.00000339585,0.008754148,0.2604573,0.002813525],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942033,0.01076741,0.003971023,0.02602801,0.005389039,0.01009293,0.0007652236,0.0004762177,0.0004771684],"genre_scores_gemma":[0.9569915,0.0002608259,0.0008704907,0.03842542,0.0001918733,0.00293591,0.0002064931,0.00005098618,0.00006651485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5286527,"threshold_uncertainty_score":0.9999588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536769443407739,"score_gpt":0.3891711967454862,"score_spread":0.3538035023114088,"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."}}