{"id":"W3080643949","doi":"10.1089/dia.2020.0357","title":"Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App","year":2020,"lang":"en","type":"article","venue":"Diabetes Technology & Therapeutics","topic":"Diabetes Management and Research","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Postprandial; Medicine; Meal; Glycemic; Carbohydrate; Diabetes mellitus; Blood Glucose Self-Monitoring; Fingerstick; Insulin; Food science; Continuous glucose monitoring; Internal medicine; Endocrinology; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001255928,0.000317995,0.0005498342,0.0004550904,0.00022973,0.00008746047,0.0003335432,0.0003015926,0.0002151195],"category_scores_gemma":[0.00005933679,0.0002673941,0.0002002243,0.001205146,0.0004890386,0.0001592116,0.0002941648,0.0005747221,0.0001960512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001329807,"about_ca_system_score_gemma":0.00005807059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004104002,"about_ca_topic_score_gemma":0.000001192482,"domain_scores_codex":[0.9977508,0.00004217165,0.00041892,0.0005372021,0.0004787002,0.0007721867],"domain_scores_gemma":[0.9987849,0.00009894735,0.0001381048,0.0005314993,0.000240937,0.0002056046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001593881,0.000775689,0.009492956,0.000077784,0.001376235,0.00001603838,0.0006371762,0.000006591524,0.7323084,0.0001241719,0.0004001772,0.2546254],"study_design_scores_gemma":[0.02589731,0.008964412,0.01326771,0.0005718287,0.003674868,0.000007429945,0.02185744,0.08333673,0.5573686,0.0005381764,0.2822534,0.002262078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645494,0.004260861,0.00343523,0.02491994,0.0002642068,0.0007982928,0.00001279156,0.000804431,0.0009548614],"genre_scores_gemma":[0.9895682,0.0002855864,0.003949746,0.005714723,0.000120288,0.0001183951,0.00006958657,0.00006331176,0.0001101118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2818532,"threshold_uncertainty_score":0.9999778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2022971654563563,"score_gpt":0.3931811392259925,"score_spread":0.1908839737696362,"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."}}