{"id":"W3024621094","doi":"10.1016/j.ijmedinf.2020.104163","title":"Predicting hospital admission for older emergency department patients: Insights from machine learning","year":2020,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Impact","funders":"McMaster University","keywords":"Emergency department; Medicine; Receiver operating characteristic; Prospective cohort study; Emergency medicine; Population; Machine learning; Geriatrics; Artificial intelligence; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001486474,0.0001234085,0.0002630342,0.00006881166,0.00006262095,0.00001367917,0.0002683252,0.00009145695,0.0005213728],"category_scores_gemma":[0.002778401,0.00008588706,0.0001924072,0.00006610174,0.00002546072,0.0002593609,0.0001161605,0.0004131699,0.000007918742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005329807,"about_ca_system_score_gemma":0.0001077228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005110294,"about_ca_topic_score_gemma":0.000001320541,"domain_scores_codex":[0.9971206,0.00001734958,0.001085791,0.00006304169,0.001593876,0.0001193173],"domain_scores_gemma":[0.9980061,0.00009392986,0.0005512384,0.00004386578,0.0009323506,0.0003725282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001636583,0.001693293,0.6057158,0.0005246325,0.00473472,0.0002382524,0.05117833,0.0001902699,0.0001078771,0.0005384102,0.1760857,0.1573561],"study_design_scores_gemma":[0.03195391,0.009248751,0.06749431,0.003935952,0.001158918,0.0001032189,0.01507541,0.2180548,0.00234509,0.001113519,0.648555,0.000961079],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658085,0.001385241,0.01376621,0.01488291,0.003254825,0.0002508461,0.00006380999,0.00002830125,0.0005593344],"genre_scores_gemma":[0.9912528,0.002440248,0.002415363,0.002038669,0.001590335,0.000005249783,0.0002262368,0.00001284703,0.00001820298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5382215,"threshold_uncertainty_score":0.5708666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429064590920512,"score_gpt":0.301117963627334,"score_spread":0.2868273177181288,"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."}}