{"id":"W3014011074","doi":"10.2337/dc19-2527","title":"Continuous Glucose Monitoring in Pregnancy: Importance of Analyzing Temporal Profiles to Understand Clinical Outcomes","year":2020,"lang":"en","type":"article","venue":"Diabetes Care","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal University Hospital; Centre Hospitalier de l’Université de Montréal; Health Sciences Centre; St Joseph's Health Centre; Ottawa Hospital; Izaak Walton Killam Health Centre; Sunnybrook Health Science Centre; Sinai Health System","funders":"Breakthrough T1D Canada; National Institute for Health and Care Research","keywords":"Medicine; Continuous glucose monitoring; Gestation; Insulin; Pregnancy; Diabetes mellitus; Blood Glucose Self-Monitoring; Type 1 diabetes; Gestational age; Internal medicine; Endocrinology; Gestational diabetes; Randomized controlled trial; Obstetrics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02335559,0.0005725365,0.000943576,0.001195786,0.0002867138,0.001716292,0.000616336,0.000562079,0.0004092894],"category_scores_gemma":[0.07313598,0.0002732549,0.0007118884,0.002902778,0.0005179437,0.002438277,0.0009140391,0.001287513,0.00008339157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005179733,"about_ca_system_score_gemma":0.001006054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003405091,"about_ca_topic_score_gemma":0.002546964,"domain_scores_codex":[0.9875157,0.009112076,0.001159521,0.0008901343,0.00112619,0.0001962789],"domain_scores_gemma":[0.9367263,0.04399747,0.01355417,0.002795445,0.002352109,0.0005744533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002469944,0.0001757458,0.8316925,0.0006669436,0.00152362,0.00009814208,0.0005167679,0.003754502,0.0007107297,0.001908565,0.001472991,0.1550096],"study_design_scores_gemma":[0.0001683572,0.001691737,0.9543589,0.0007658332,0.0007645818,0.000387114,0.0007146294,0.02294408,0.0010284,0.01077634,0.006317818,0.00008218648],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8098763,0.04439706,0.1233389,0.007145446,0.0004861974,0.0004869804,0.005345101,0.0001729287,0.008751035],"genre_scores_gemma":[0.9594654,0.006142891,0.03094735,0.0008884555,0.0004088687,0.0004874879,0.001363035,0.00004452525,0.00025192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02335559,"threshold_uncertainty_score":0.1235176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05531996585452901,"score_gpt":0.3541125403454755,"score_spread":0.2987925744909464,"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."}}