{"id":"W4283008433","doi":"10.2196/36958","title":"Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Diabetes Management and Research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hypoglycemia; Medicine; Type 2 diabetes; Machine learning; Artificial intelligence; Diabetes mellitus; Receiver operating characteristic; Computer science; Type 1 diabetes; Health records; Pediatrics; Intensive care medicine; Health care; Endocrinology","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":[0.001088969,0.00009429127,0.0002358799,0.0001876315,0.0001169759,0.000009424039,0.00008690979,0.00003938565,0.0001636142],"category_scores_gemma":[0.0001883779,0.00007180231,0.00001423487,0.0003835403,0.00005084663,0.00007492435,0.0001188919,0.0006776314,0.000002130676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001737948,"about_ca_system_score_gemma":0.0003946142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001251152,"about_ca_topic_score_gemma":0.000006319196,"domain_scores_codex":[0.9980142,0.00009545828,0.0004809751,0.00008163573,0.0009912041,0.0003365045],"domain_scores_gemma":[0.9993482,0.00009362291,0.0002563788,0.00009667486,0.00005270924,0.0001524538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001760602,0.0002350477,0.9268355,0.0004360641,0.00004663146,3.385434e-7,0.0008691405,0.00004302288,8.966974e-7,0.000005613163,0.0005281056,0.07082357],"study_design_scores_gemma":[0.02262691,0.01585178,0.5538368,0.0008878463,0.00009370199,7.287482e-7,0.002128724,0.3078698,0.0003886988,0.00004033532,0.09575981,0.0005148664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987811,0.00009378141,0.00005843227,0.0001898516,0.00002612956,0.0005614255,0.000005253298,0.00002690956,0.0002571287],"genre_scores_gemma":[0.9981592,0.00003840154,0.0007444201,0.0002977822,0.000007912164,0.00005284503,0.0005631141,0.00001211165,0.0001242694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3729987,"threshold_uncertainty_score":0.2944009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00771641325046277,"score_gpt":0.2593972263264506,"score_spread":0.2516808130759878,"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."}}