{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001323461,0.0002475909,0.0006388638,0.0001369079,0.0002735173,0.000007695058,0.0004369895,0.0004510935,0.0005920429],"category_scores_gemma":[0.004112062,0.0002090192,0.00007001525,0.000673221,0.0001183249,0.0003024481,0.0002350826,0.00227537,0.0001772987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006317855,"about_ca_system_score_gemma":0.002235028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004091299,"about_ca_topic_score_gemma":0.001475918,"domain_scores_codex":[0.9938794,0.000671223,0.003228403,0.0002479032,0.001057454,0.0009156468],"domain_scores_gemma":[0.9959087,0.001433223,0.001378716,0.0003215917,0.0003016487,0.0006561647],"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":[0.0003891292,0.0002742629,0.2569217,0.00493497,0.00002438329,0.000006119546,0.1390927,0.0001082993,0.00001315889,0.0001728185,0.003883616,0.5941788],"study_design_scores_gemma":[0.001534183,0.0005376692,0.006839192,0.009309136,0.000008605304,0.000001231621,0.03204173,0.9451127,0.0004391382,0.0008189755,0.003068594,0.0002888608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717876,0.0004970364,0.01915566,0.004326942,0.00103872,0.002549866,0.0003001866,0.0002296456,0.0001143921],"genre_scores_gemma":[0.9938194,0.0004663447,0.002572858,0.001951585,0.0004722286,0.0001986862,0.0004841553,0.00002829598,0.000006482072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9450044,"threshold_uncertainty_score":0.9885478,"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."}}