{"id":"W4387338058","doi":"10.1016/j.cjca.2023.06.162","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 Cardiology","topic":"Diabetes Treatment and Management","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; Heart failure; Type 2 diabetes; Nursing; Internal medicine; Pathology; Endocrinology","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":[0.01151509,0.0003851701,0.0004782786,0.0009076426,0.0006349936,0.003028876,0.0007971154,0.0007552203,0.001856158],"category_scores_gemma":[0.02773697,0.0001352405,0.0005683722,0.0009496717,0.0002932725,0.0007273663,0.001520627,0.001571454,0.0002626855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003298968,"about_ca_system_score_gemma":0.01211521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02457541,"about_ca_topic_score_gemma":0.03894706,"domain_scores_codex":[0.9936157,0.004062869,0.0003962404,0.0003171254,0.001318988,0.0002889989],"domain_scores_gemma":[0.9794936,0.01095922,0.002704503,0.0004233762,0.003033501,0.003385823],"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":[0.001206054,0.002665437,0.08862802,0.0007107726,0.0004643444,0.0002421374,0.0009496721,0.0117995,0.0006625729,0.003928815,0.09755468,0.791188],"study_design_scores_gemma":[0.004540863,0.007673323,0.4599551,0.004361017,0.001430596,0.0008660838,0.004023193,0.2556103,0.005602797,0.0264198,0.2291875,0.0003294539],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.400463,0.01930554,0.0636147,0.424143,0.001944715,0.001868331,0.003348134,0.001906991,0.08340549],"genre_scores_gemma":[0.855983,0.008620623,0.1088621,0.01768623,0.0009482614,0.0005872417,0.002721874,0.0001188235,0.004471896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02457541,"threshold_uncertainty_score":0.06089836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08054182740256316,"score_gpt":0.3956789276709912,"score_spread":0.3151371002684281,"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."}}