{"id":"W4412603708","doi":"10.1016/j.engappai.2025.111758","title":"A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Diabetes Management and Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Institute of General Medical Sciences; Leona M. and Harry B. Helmsley Charitable Trust; Ministero dell’Istruzione, dell’Università e della Ricerca; European Commission; Nutrition Obesity Research Center, University of North Carolina; Louisiana Clinical and Translational Science Center; National Institute of Diabetes and Digestive and Kidney Diseases; Dexcom","keywords":"Computer science; Type 2 diabetes; Type (biology); Energy expenditure; Diabetes mellitus; Medicine; 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":[0.0005323853,0.0006787468,0.0004918303,0.0004635471,0.0002397335,0.0004668131,0.001011051,0.0007243065,0.002020784],"category_scores_gemma":[0.00183589,0.0003291232,0.0006084238,0.0006109637,0.0002221694,0.0006844181,0.0005842258,0.001091452,0.0003900284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008467529,"about_ca_system_score_gemma":0.0006465812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03380615,"about_ca_topic_score_gemma":0.03537785,"domain_scores_codex":[0.9997949,0.00005400158,0.00001186159,0.00008099063,0.00002475303,0.00003344839],"domain_scores_gemma":[0.9996824,0.0001866214,0.00003818994,0.00001267129,0.0000666683,0.00001349902],"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":[0.0001342075,0.00005300535,0.004495928,0.00005138112,0.00008537087,0.00008471863,0.00006769016,0.9542631,0.0008942138,0.00313013,0.001440217,0.03529997],"study_design_scores_gemma":[0.000002463543,0.000007872857,0.0006741809,0.000004358316,0.000008439571,0.000005560762,0.000003629665,0.9980983,0.00004542137,0.001039727,0.0001071737,0.00000286591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1822017,0.001765705,0.8056009,0.001296048,0.0002139845,0.00007518569,0.002738894,0.0009687906,0.005138821],"genre_scores_gemma":[0.9529902,0.0005663472,0.03926307,0.0001904164,0.00007339947,0.0002023434,0.001795646,0.00004577509,0.004872734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03380615,"threshold_uncertainty_score":0.06721872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02095210214255811,"score_gpt":0.3034763945550655,"score_spread":0.2825242924125074,"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."}}