{"id":"W3199064092","doi":"10.2337/dci21-0018","title":"Improving Prediction of Risk for the Development of Type 1 Diabetes—Insights From Populations at High Risk","year":2021,"lang":"en","type":"article","venue":"Diabetes Care","topic":"Diabetes and associated disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Diabetes mellitus; Type 2 diabetes; Type 1 diabetes; Human leukocyte antigen; Autoantibody; Disease; Internal medicine; Bioinformatics; Immunology; Endocrinology; Antibody; Antigen","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":[0.003043934,0.0007802804,0.0009771517,0.00161427,0.0007593597,0.002038165,0.0009004827,0.001552542,0.001233505],"category_scores_gemma":[0.01213274,0.0003301332,0.0006970061,0.00159329,0.0003210531,0.001885277,0.001386715,0.003121214,0.0004844686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006982863,"about_ca_system_score_gemma":0.001041773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01207695,"about_ca_topic_score_gemma":0.01276173,"domain_scores_codex":[0.9988707,0.0004967268,0.00009535153,0.0001747277,0.0002582451,0.0001041579],"domain_scores_gemma":[0.9970164,0.00113759,0.0004162749,0.0001845782,0.0006932218,0.0005519287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002943942,0.0003992443,0.7661179,0.0001967772,0.0003883649,0.0002422015,0.001016264,0.0004229502,0.000228156,0.0005792556,0.01863722,0.2114773],"study_design_scores_gemma":[0.0001167229,0.0008105098,0.952159,0.0008932515,0.0007682532,0.0008862845,0.002614737,0.004788635,0.0003029321,0.01060107,0.02591238,0.0001461401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7619103,0.1002436,0.01260102,0.09634677,0.003018707,0.0002522233,0.004333809,0.0003200205,0.02097367],"genre_scores_gemma":[0.9128034,0.0509883,0.0128371,0.01464421,0.004435162,0.0001820181,0.002409725,0.00007376401,0.001626471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01207695,"threshold_uncertainty_score":0.02401328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007412766221642522,"score_gpt":0.2062781699741427,"score_spread":0.1988654037525001,"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."}}