{"id":"W3016344200","doi":"10.1089/dia.2019.0458","title":"Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis","year":2020,"lang":"en","type":"article","venue":"Diabetes Technology & Therapeutics","topic":"Diabetes Management and Research","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Hypoglycemia; Medicine; Bedtime; Nocturnal; Type 1 diabetes; Diabetes mellitus; Receiver operating characteristic; Confidence interval; Artificial pancreas; Internal medicine; Endocrinology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004867701,0.001112896,0.001067767,0.001094696,0.0003375531,0.001060819,0.0007053804,0.0006344791,0.0004651197],"category_scores_gemma":[0.01316359,0.0003302427,0.000927754,0.000475825,0.0005607709,0.0008925765,0.0005798764,0.001256826,0.00006669769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040638,"about_ca_system_score_gemma":0.00135983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006994394,"about_ca_topic_score_gemma":0.003155263,"domain_scores_codex":[0.999006,0.0006131645,0.0000651001,0.000102684,0.0001503679,0.00006264426],"domain_scores_gemma":[0.9870772,0.01111135,0.0007211599,0.0002156756,0.0006308614,0.0002436836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002670077,0.00018052,0.01508903,0.0000951398,0.0001542693,0.00006609489,0.00003941388,0.9558007,0.0002174217,0.001243179,0.0005033087,0.02634389],"study_design_scores_gemma":[0.00001120967,0.00006682127,0.001071816,0.00001136096,0.00001378961,0.000009210669,0.00001396983,0.9961803,0.0001420282,0.002425595,0.00004820622,0.000005581132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6011038,0.002862239,0.3885858,0.00357277,0.000156791,0.0002052854,0.0005019278,0.0005764921,0.00243493],"genre_scores_gemma":[0.9760665,0.0003544816,0.02296115,0.0001352749,0.00004835087,0.00005748037,0.0002212863,0.000008924704,0.0001465228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006994394,"threshold_uncertainty_score":0.02574319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08514565126685408,"score_gpt":0.327203361074498,"score_spread":0.242057709807644,"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."}}