{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001075916,0.0008223762,0.0005813828,0.004020221,0.0004248344,0.001036469,0.0008017661,0.0006702959,0.01201682],"category_scores_gemma":[0.01127411,0.0002854608,0.0005054488,0.001891808,0.0000835777,0.0008542759,0.0008761983,0.0005982775,0.004365908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006552054,"about_ca_system_score_gemma":0.001294188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01904273,"about_ca_topic_score_gemma":0.02262594,"domain_scores_codex":[0.999132,0.0001760743,0.0001232695,0.0001226104,0.000376603,0.00006945513],"domain_scores_gemma":[0.9932243,0.004230072,0.0006057201,0.0002837241,0.001185321,0.0004709046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001280586,0.001070783,0.2441937,0.0003187445,0.00020697,0.0008599233,0.000293311,0.005475238,0.001853158,0.001598415,0.1013005,0.6415486],"study_design_scores_gemma":[0.001013487,0.00113702,0.5187896,0.0008253452,0.0008534998,0.005064433,0.0007364802,0.2781357,0.014488,0.01252518,0.1659376,0.0004936164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.482886,0.004368956,0.2127328,0.00499335,0.0008405226,0.003251924,0.1147771,0.1109876,0.06516188],"genre_scores_gemma":[0.7053086,0.001785412,0.2275651,0.001089538,0.0003374794,0.001299914,0.04214429,0.0007324506,0.0197372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01904273,"threshold_uncertainty_score":0.04020029,"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."}}