{"id":"W3123700022","doi":"10.2196/22458","title":"Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Diabetes Management and Research","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Hypoglycemia; False positive paradox; Algorithm; Medicine; Diabetes mellitus; Meta-analysis; Machine learning; Receiver operating characteristic; Confidence interval; Artificial intelligence; Internal medicine; Computer science; 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":[0.0006832944,0.0002742055,0.00131584,0.0005767998,0.00005606849,0.0000465251,0.0001357568,0.00007103275,0.0003079448],"category_scores_gemma":[0.0003155142,0.000191052,0.0003805643,0.001777807,0.0001311173,0.0001213045,0.0002548404,0.0003791228,0.000007272241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005352664,"about_ca_system_score_gemma":0.00004621122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000585422,"about_ca_topic_score_gemma":0.00002157067,"domain_scores_codex":[0.9973667,0.0002372987,0.0004733721,0.0006257562,0.0007372967,0.0005595725],"domain_scores_gemma":[0.9985317,0.0002948226,0.000123456,0.0004944113,0.0003072511,0.0002483525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006136377,0.0004693765,0.9507886,0.0005592063,0.01369901,0.000002541226,0.00009577131,0.0001009437,0.0002995341,0.000003635361,0.00003330062,0.03388678],"study_design_scores_gemma":[0.001769675,0.0008827977,0.9700593,0.00006965229,0.01923568,1.356819e-8,0.00002601383,0.002742267,0.003926543,0.00005409347,0.001004418,0.0002295312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914266,0.007143255,0.00002366754,0.0002276247,0.0000289364,0.0009063953,0.00005046479,0.0000395155,0.0001535652],"genre_scores_gemma":[0.9984631,0.0000355183,0.0007989664,0.00006513261,0.00002399348,0.0002076247,0.0001735361,0.00002778161,0.0002043377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03365725,"threshold_uncertainty_score":0.7790876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673574529550676,"score_gpt":0.2963578665055502,"score_spread":0.2696221212100434,"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."}}