{"id":"W3033642885","doi":"10.2196/18963","title":"Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Glycated hemoglobin; Population; Computer science; Medicine; Body mass index; Statistics; Predictive modelling; Diabetes mellitus; Data mining; Machine learning; Internal medicine; Type 2 diabetes; Mathematics; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0236182,0.001381167,0.0009179627,0.00164629,0.000458863,0.001112603,0.002048003,0.001107347,0.0009793829],"category_scores_gemma":[0.04159387,0.0003850082,0.001769085,0.00130235,0.0003979799,0.001272021,0.001233554,0.00174876,0.0004823893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008351034,"about_ca_system_score_gemma":0.001902078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01319271,"about_ca_topic_score_gemma":0.005935721,"domain_scores_codex":[0.9943942,0.003461161,0.0004935564,0.0008937307,0.0005599025,0.0001974737],"domain_scores_gemma":[0.9722012,0.02211605,0.001140294,0.0014661,0.002761104,0.000315279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002225277,0.0026986,0.5595231,0.0004925253,0.001597595,0.0005368325,0.0004631044,0.2394427,0.002017538,0.0008970734,0.003088595,0.187017],"study_design_scores_gemma":[0.000138661,0.0007618334,0.04059325,0.00008037541,0.0002075492,0.0001389634,0.0001625081,0.955296,0.001489953,0.000532803,0.0005683784,0.00002962268],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495108,0.0009973843,0.04577997,0.0006438388,0.0001083034,0.0003428294,0.001109311,0.000526734,0.0009807629],"genre_scores_gemma":[0.9519101,0.0004225189,0.0453416,0.00008561264,0.00004404381,0.0002448033,0.001512519,0.00003126091,0.0004074498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0236182,"threshold_uncertainty_score":0.1249065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.151804314179579,"score_gpt":0.4637008690505904,"score_spread":0.3118965548710115,"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."}}