{"id":"W4405551762","doi":"10.1002/ncp.11254","title":"Quality improvement for parenteral nutrition in hospital: Applying a gap analysis to an electronic health record to review parenteral nutrition processing","year":2024,"lang":"en","type":"review","venue":"Nutrition in Clinical Practice","topic":"Clinical Nutrition and Gastroenterology","field":"Nursing","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Alexandra Hospital; Alberta Health Services; University of Alberta","funders":"","keywords":"Medicine; Patient safety; Clinical decision support system; Order entry; Electronic health record; Pharmacist; Quality (philosophy); EPIC; Parenteral nutrition; Quality management; Compounding; Risk analysis (engineering); Health care; Medical emergency; Operations management; Intensive care medicine; Decision support system; Nursing; Pharmacy; Computer science; Data mining; Engineering","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01030358,0.001021717,0.005752775,0.001833029,0.0002033245,0.0003702598,0.0007303923,0.0008532032,0.00003420622],"category_scores_gemma":[0.006434382,0.001059834,0.002367535,0.003829019,0.00009361777,0.0009526195,0.0002020611,0.003123264,0.00007038128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002324707,"about_ca_system_score_gemma":0.0001951098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003223845,"about_ca_topic_score_gemma":0.002254457,"domain_scores_codex":[0.980659,0.004831859,0.009156067,0.002882528,0.0008137494,0.001656773],"domain_scores_gemma":[0.9911812,0.003711325,0.002619315,0.001012485,0.0006244844,0.0008511815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002216102,0.02254465,0.00009555527,0.1449545,0.0003500314,0.00002028257,0.00009288274,0.000001115921,6.19299e-7,0.00003277696,0.03709203,0.7925994],"study_design_scores_gemma":[0.004132944,0.00943486,0.00005482333,0.1208056,0.002824048,0.0000191048,0.0003195388,0.00009281403,1.995756e-7,0.0009458707,0.8606353,0.000734826],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002567859,0.8446323,0.001897771,0.1279208,0.001963644,0.02242122,0.0006674621,0.0002325904,0.000007474868],"genre_scores_gemma":[0.0007815255,0.917116,0.00863648,0.04210971,0.001613413,0.02592506,0.003656803,0.0001546256,0.000006363151],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8235433,"threshold_uncertainty_score":0.9991852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1762240403501581,"score_gpt":0.5616411946552466,"score_spread":0.3854171543050885,"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."}}