{"id":"W4387934233","doi":"10.1016/j.jcjd.2023.10.154","title":"INTEGRATING ARTIFICIAL INTELLIGENCE FOR QUALITY IMPROVEMENT: SGLT2-INHIBITOR INITIATION FOR PATIENTS MEETING CLINICAL PRACTICE GUILDELINE CRITERIA. A NURSE PRACTITIONER PATIENT OPTIMIZATION INITIATIVE","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Calgary Laboratory Services","funders":"","keywords":"Medicine; Guideline; Coronary artery disease; Clinical Practice; Intensive care medicine; Ejection fraction; Diabetes mellitus; Type 2 diabetes; Heart failure; Nursing; Internal medicine; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01183455,0.0004115347,0.0004584593,0.001001522,0.0006838461,0.003314396,0.0008364198,0.0007494225,0.001819062],"category_scores_gemma":[0.02829088,0.0001350834,0.0005616561,0.001043978,0.0003321407,0.0007884875,0.001611599,0.001502887,0.0002506571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003373876,"about_ca_system_score_gemma":0.0117394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03023576,"about_ca_topic_score_gemma":0.04499331,"domain_scores_codex":[0.99374,0.004055984,0.0003606734,0.0003239416,0.00124178,0.0002776666],"domain_scores_gemma":[0.9793633,0.01213882,0.002441876,0.000472711,0.00297268,0.002610528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001200434,0.002493938,0.08581694,0.0006956388,0.0004487658,0.0002278591,0.0008964414,0.01826815,0.0007655508,0.004615381,0.07707484,0.807496],"study_design_scores_gemma":[0.003523502,0.006539755,0.3493277,0.003403506,0.001379265,0.0007234376,0.003938464,0.3885247,0.007427027,0.03027107,0.2045858,0.0003559179],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4522106,0.01779301,0.08851149,0.3421722,0.001812538,0.001900013,0.003671412,0.002527456,0.08940124],"genre_scores_gemma":[0.8655427,0.006381998,0.1086587,0.01193985,0.000677613,0.0004439388,0.002522026,0.0001064668,0.003726769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03023576,"threshold_uncertainty_score":0.0625878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1886731724273322,"score_gpt":0.4650039363254005,"score_spread":0.2763307638980683,"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."}}