{"id":"W2896927272","doi":"10.2196/10212","title":"Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study","year":2018,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Algorithm","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001267789,0.0002184023,0.0004416105,0.0002050136,0.001358098,0.00001012837,0.0002148951,0.000125058,0.0005624571],"category_scores_gemma":[0.0006486359,0.0001926984,0.00005668856,0.0005023698,0.0001742664,0.0002106988,0.0001949615,0.0009229447,0.0001974242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009536532,"about_ca_system_score_gemma":0.0001403513,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008978549,"about_ca_topic_score_gemma":0.002292788,"domain_scores_codex":[0.9961451,0.001518693,0.0009171055,0.0003935962,0.0003697082,0.0006558349],"domain_scores_gemma":[0.9977132,0.0005904731,0.0005827029,0.0004065437,0.0005610409,0.0001460592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004358991,0.0005139848,0.972684,0.00009597189,0.00005712088,6.473079e-7,0.008314644,0.00002757152,0.00200976,0.00002454605,0.00005306486,0.0161751],"study_design_scores_gemma":[0.001074785,0.00625076,0.6032294,0.0004174113,0.0002455706,3.819676e-7,0.02264129,0.3521774,0.006000171,0.001504098,0.006012879,0.0004459039],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947654,0.0008557258,0.0004951915,0.00006276937,0.001134022,0.002169079,0.0001826208,0.0001997368,0.000135429],"genre_scores_gemma":[0.9970117,0.00005321948,0.00130525,0.00007780827,0.0009950772,0.000323759,0.0000224101,0.00005761757,0.0001531609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3694546,"threshold_uncertainty_score":0.999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1394892210532136,"score_gpt":0.4368438856149038,"score_spread":0.2973546645616902,"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."}}