{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02818174,0.0004739225,0.0008471612,0.001861108,0.001080837,0.001666603,0.001167914,0.001256187,0.001273851],"category_scores_gemma":[0.05145205,0.0007980962,0.002123636,0.005061468,0.0007260159,0.001229448,0.001595732,0.00293342,0.0002615392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142404,"about_ca_system_score_gemma":0.003044127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02560809,"about_ca_topic_score_gemma":0.02542401,"domain_scores_codex":[0.9805115,0.01421909,0.001361366,0.001338764,0.001826665,0.0007426313],"domain_scores_gemma":[0.9654707,0.01846443,0.007521903,0.005253424,0.002106067,0.001183455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001846524,0.000229058,0.9924538,0.00007712522,0.0006693911,0.00007626879,0.0006385392,0.0006215543,0.00005630431,0.0005109388,0.0006496465,0.003832748],"study_design_scores_gemma":[0.0002156903,0.0008279547,0.9787782,0.0003388137,0.000790799,0.0003366776,0.002146618,0.01211832,0.0001478235,0.002235619,0.002001374,0.00006202014],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870515,0.0007294243,0.006371539,0.001030963,0.00006700956,0.0003922607,0.003645682,0.00002505095,0.0006866316],"genre_scores_gemma":[0.9874576,0.0004742943,0.007894624,0.000216102,0.00003930168,0.0006077146,0.00309595,0.00001009327,0.0002043869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02818174,"threshold_uncertainty_score":0.149041,"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."}}