{"id":"W2125199656","doi":"10.1109/tbme.2011.2176939","title":"Control-Relevant Models for Glucose Control Using A Priori Patient Characteristics","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Diabetes Management and Research","field":"Medicine","cited_by":161,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Artificial pancreas; Hypoglycemia; Postprandial; A priori and a posteriori; Robustness (evolution); Control theory (sociology); Computer science; Model predictive control; Type 1 diabetes; Glycemic; Control system; Controller (irrigation); Artificial intelligence; Machine learning; Medicine; Insulin; Control (management); Diabetes mellitus; Engineering; 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":[0.001031642,0.001085309,0.0008047562,0.0004051696,0.0003244421,0.001077737,0.001097825,0.001132369,0.00175802],"category_scores_gemma":[0.004132465,0.0004358185,0.0009112287,0.0004392484,0.0005297566,0.0008358498,0.0007483028,0.00175824,0.0005183645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006123713,"about_ca_system_score_gemma":0.001100503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006014943,"about_ca_topic_score_gemma":0.003398237,"domain_scores_codex":[0.9994348,0.000187134,0.00003314922,0.0001210027,0.0001767593,0.00004717538],"domain_scores_gemma":[0.998919,0.0006149833,0.0001738688,0.00008405068,0.0001861849,0.00002193417],"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":[0.00003198154,0.00001867105,0.0002350648,0.00003615009,0.00001277487,0.00003483945,0.00002857074,0.9858943,0.0007251427,0.005852337,0.0002404958,0.006889684],"study_design_scores_gemma":[0.000007805079,0.0000218533,0.0001180405,0.000005642521,0.000008966601,0.00001029964,0.000003036512,0.9963338,0.0003159491,0.002676994,0.0004921576,0.000005449779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009299521,0.0002727919,0.9874445,0.0001800512,0.00004975116,0.00005230636,0.0001573189,0.0003036227,0.002240118],"genre_scores_gemma":[0.8806882,0.001000453,0.1118347,0.000239473,0.0001006583,0.0005324429,0.0006991559,0.000105454,0.004799405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006014943,"threshold_uncertainty_score":0.01195985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03160175084235151,"score_gpt":0.2489106508445361,"score_spread":0.2173089000021846,"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."}}