{"id":"W3181767717","doi":"10.3390/diabetology2030011","title":"An Online Risk Tool for Predicting Type 2 Diabetes Mellitus","year":2021,"lang":"en","type":"article","venue":"Diabetology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Sinai Health System; University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Logistic regression; Calculator; Body mass index; Diabetes mellitus; Type 2 Diabetes Mellitus; Medicine; Medical record; Risk assessment; Type 2 diabetes; Internal medicine; Computer science; Gerontology; Endocrinology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007786427,0.000168915,0.0004562285,0.00007100322,0.0008695048,0.000008451335,0.0002224484,0.0004550928,0.0005706416],"category_scores_gemma":[0.004189603,0.0001675351,0.00007393045,0.000273572,0.00009240143,0.0001170356,0.00008916049,0.0007322623,0.0002475597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001028201,"about_ca_system_score_gemma":0.0005523438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002613801,"about_ca_topic_score_gemma":0.002749133,"domain_scores_codex":[0.9965283,0.001132802,0.00074595,0.0004974851,0.0001230022,0.0009724869],"domain_scores_gemma":[0.9952212,0.002886011,0.0002706906,0.0005789555,0.0008632847,0.0001798355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003113607,0.000113953,0.9603248,0.0002026824,0.00003449562,0.000005394726,0.001580472,0.00003400298,0.001027217,0.002084721,0.001050533,0.03351054],"study_design_scores_gemma":[0.001457395,0.002403976,0.5574836,0.0006341393,0.0003905646,0.000005028941,0.02689398,0.1132019,0.02068161,0.06479034,0.2105261,0.001531435],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915482,0.001688365,0.000385342,0.00151106,0.003330516,0.0007453228,0.0003611936,0.0002113077,0.0002186249],"genre_scores_gemma":[0.990881,0.0003235635,0.003966682,0.002488907,0.001262751,0.0002335166,0.0005432733,0.00005300847,0.0002472753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4028413,"threshold_uncertainty_score":0.6831883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.14547705577654,"score_gpt":0.4740280633845178,"score_spread":0.3285510076079778,"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."}}