{"id":"W3096124818","doi":"10.2196/22912","title":"Using Structural Equation Modelling in Routine Clinical Data on Diabetes and Depression: Observational Cohort Study","year":2020,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Diabetes Management and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Medicine; Observational study; Depression (economics); Latent variable; Clinical trial; Cohort; Type 2 diabetes; Health care; Diabetes mellitus; Computer science; Artificial intelligence; Machine learning; Pathology","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":[0.000610947,0.0000895102,0.0002147096,0.00005018384,0.00004140456,0.00002856533,0.00009344044,0.00004079857,0.00003327769],"category_scores_gemma":[0.000472956,0.00007685084,0.00001529065,0.000166494,0.00001818961,0.0002083441,0.0001081334,0.000145381,0.000004264856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002535469,"about_ca_system_score_gemma":0.00003005168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001358721,"about_ca_topic_score_gemma":0.000002422053,"domain_scores_codex":[0.9988335,0.00007289613,0.0003454838,0.0003538668,0.0002644855,0.0001297152],"domain_scores_gemma":[0.9993998,0.0001250703,0.00009032955,0.0002542206,0.00004341259,0.00008714115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006721083,0.00008713698,0.9954948,0.00002663651,0.00003595879,0.000001580462,0.0002448045,0.00180338,0.00003339052,0.00001649475,0.000467301,0.001721237],"study_design_scores_gemma":[0.0007164064,0.00007782889,0.5252528,0.00002857446,0.00004992459,3.999954e-8,0.0001299951,0.4736233,0.000008687917,0.00003018583,0.00003997056,0.00004226305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962396,0.00003787541,0.0004104224,0.002177439,0.0001826804,0.0008019053,0.000003113216,0.00002669645,0.0001202676],"genre_scores_gemma":[0.9965962,0.000003818567,0.001830099,0.0006492278,0.0004452188,0.00001495524,0.0003847756,0.00001098152,0.00006475933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4718199,"threshold_uncertainty_score":0.3133886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4881511480250048,"score_gpt":0.4495414133083742,"score_spread":0.03860973471663059,"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."}}