{"id":"W4280601853","doi":"10.1016/j.outlook.2022.03.011","title":"American Academy of Nursing on Policy Social Determinants of Health: Data Standardization in Electronic Health Records","year":2022,"lang":"en","type":"article","venue":"Nursing Outlook","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"General Dynamics (Canada)","funders":"","keywords":"Standardization; Health records; Nursing; Medical record; Gerontology; Medicine; Political science; Health care; Law","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00287143,0.0001652471,0.0007639439,0.00065976,0.001870805,0.000003572504,0.0004565857,0.0001027769,0.00008808205],"category_scores_gemma":[0.0001886067,0.0001997405,0.00005542803,0.001195524,0.0002322078,0.0001386047,0.0001150801,0.00128581,0.000004523797],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004434139,"about_ca_system_score_gemma":0.00431722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00502255,"about_ca_topic_score_gemma":0.0009535347,"domain_scores_codex":[0.9942102,0.002269196,0.001488528,0.0004227499,0.0005995082,0.001009838],"domain_scores_gemma":[0.9975506,0.0002195521,0.001696247,0.0003560066,0.00005496753,0.0001225959],"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":[0.001140809,0.001742528,0.0676919,0.0009618692,0.00002804607,7.561515e-7,0.159774,0.0006000665,0.00002518208,0.04332751,0.02511222,0.6995951],"study_design_scores_gemma":[0.007738696,0.007517381,0.7281215,0.01030836,0.00008421412,0.00001108352,0.1103093,0.01126038,0.0000610089,0.03189706,0.09155079,0.001140203],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8193015,0.00157875,0.0007483323,0.1662376,0.001591866,0.003388302,0.003049326,0.0001663376,0.003937935],"genre_scores_gemma":[0.9962138,0.0002496781,0.0003460323,0.002574901,0.0002307171,0.00003696315,0.0002323343,0.0000367693,0.00007878733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6984549,"threshold_uncertainty_score":0.9994286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3410099156843159,"score_gpt":0.5881298873793646,"score_spread":0.2471199716950486,"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."}}