{"id":"W3035213812","doi":"10.2337/db20-265-or","title":"265-OR: Identifying Adults at Risk of Unintentional Severe Hypoglycemia in Hospital Using Artificial Intelligence (RUSHH-AI)","year":2020,"lang":"en","type":"article","venue":"Diabetes","topic":"Hyperglycemia and glycemic control in critically ill and hospitalized patients","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Hypoglycemia; Machine learning; Artificial intelligence; Emergency medicine; Artificial neural network; Health care; Diabetes mellitus; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001596108,0.0006702671,0.0005345038,0.0008114374,0.0001708627,0.0008946958,0.0007807963,0.0003629234,0.003302542],"category_scores_gemma":[0.005252116,0.0001386096,0.0009297556,0.0006726893,0.0001510077,0.0003306233,0.000652796,0.0004764834,0.0009443968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193749,"about_ca_system_score_gemma":0.001608381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04167844,"about_ca_topic_score_gemma":0.03738506,"domain_scores_codex":[0.9993555,0.0002229379,0.00007175859,0.0001231759,0.0001415776,0.00008512803],"domain_scores_gemma":[0.9985751,0.0005833967,0.0003303584,0.0001051929,0.0002794522,0.0001264975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001031435,0.0004761874,0.7936707,0.0005460671,0.0007924314,0.000365178,0.000201322,0.06106727,0.0004109092,0.001102058,0.02668489,0.1136515],"study_design_scores_gemma":[0.0001899102,0.001378382,0.3449387,0.0003131427,0.0006901015,0.0005801596,0.0005030588,0.6356565,0.0008077619,0.003461162,0.01141167,0.00006944691],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9043522,0.002237137,0.03958122,0.004213311,0.0002946679,0.0005862658,0.02916114,0.002344163,0.01722986],"genre_scores_gemma":[0.9730166,0.0006409414,0.00972991,0.0004270279,0.00009965098,0.0001632965,0.01373042,0.0000367527,0.002155376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04167844,"threshold_uncertainty_score":0.08287168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03687547466522328,"score_gpt":0.2948371897429957,"score_spread":0.2579617150777724,"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."}}