{"id":"W2232865562","doi":"10.1016/j.annemergmed.2015.07.308","title":"274 The Impact of Computerized Provider Order Entry on Emergency Department Flow","year":2015,"lang":"en","type":"article","venue":"Annals of Emergency Medicine","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre","funders":"","keywords":"Medicine; Emergency department; Triage; Emergency medicine; Order entry; Retrospective cohort study; Subgroup analysis; Computerized physician order entry; Medical emergency; Health care; Pediatrics; Internal medicine; Meta-analysis; Nursing","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":[0.004698805,0.000311012,0.0003957555,0.001301322,0.0009783417,0.002584207,0.001215408,0.0009794981,0.01872659],"category_scores_gemma":[0.08450782,0.0003475108,0.001400076,0.001753199,0.0007572962,0.00210878,0.001191063,0.001786797,0.001303873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00245266,"about_ca_system_score_gemma":0.005026498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02629389,"about_ca_topic_score_gemma":0.02389406,"domain_scores_codex":[0.9921022,0.005222128,0.0003390089,0.0003544285,0.001170344,0.0008119149],"domain_scores_gemma":[0.8805197,0.099558,0.008411821,0.002304949,0.004818595,0.004387057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007193485,0.004360409,0.9423212,0.00011303,0.0004328724,0.0002845357,0.0005150885,0.001901605,0.0002684067,0.000822659,0.002759492,0.03902733],"study_design_scores_gemma":[0.0002824557,0.001791717,0.9912673,0.00008831356,0.0002800896,0.00009739333,0.00128678,0.002709671,0.0004273822,0.0004164774,0.001322467,0.00003015373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894496,0.0002690318,0.0001398103,0.001401145,0.0000905557,0.00004733741,0.0007789294,0.00001838432,0.007805195],"genre_scores_gemma":[0.9974582,0.0001647359,0.0001996641,0.0002286695,0.00009428903,0.00001993794,0.000504275,0.00001348168,0.001316712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02629389,"threshold_uncertainty_score":0.06264669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1428885945010725,"score_gpt":0.4249875826101632,"score_spread":0.2820989881090906,"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."}}