{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002029931,0.0004247202,0.0003693276,0.0003385694,0.000215457,0.001015323,0.0003437002,0.0004709986,0.006741863],"category_scores_gemma":[0.009959619,0.0001763183,0.0003396635,0.0001640515,0.0001755352,0.0007826068,0.0006566416,0.0003459949,0.001163784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001426371,"about_ca_system_score_gemma":0.0002221368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004274348,"about_ca_topic_score_gemma":0.0009427821,"domain_scores_codex":[0.9984415,0.0008085771,0.0001610999,0.0002482343,0.0002563744,0.00008420019],"domain_scores_gemma":[0.9936674,0.003922992,0.001078554,0.000237258,0.0007378201,0.0003559657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008773341,0.003274502,0.5772107,0.002299684,0.0004260801,0.0007478491,0.007247621,0.0006603369,0.01514644,0.0001726163,0.007105681,0.3769352],"study_design_scores_gemma":[0.001458162,0.01899542,0.9114542,0.001316181,0.001470652,0.004931855,0.01301688,0.01559657,0.01227107,0.0006077603,0.01850177,0.0003795225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901962,0.0004248782,0.003640821,0.000386289,0.0000511751,0.0004078329,0.0005568257,0.0002068486,0.004129141],"genre_scores_gemma":[0.9850381,0.0003655681,0.01256663,0.0004591701,0.00007277046,0.000511702,0.000207336,0.00001966723,0.0007590645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006741863,"threshold_uncertainty_score":0.02255374,"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."}}