{"id":"W4391618281","doi":"10.1101/2024.02.03.24302303","title":"Predicting Depression in Canadians with or at Risk of Diabetes: A Cross-Sectional Machine Learning Analysis","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Diabetes Management and Education","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Machine learning; Depression (economics); Artificial intelligence; Logistic regression; Random forest; Psychological intervention; Predictive power; Cohort; Naive Bayes classifier; Body mass index; Medicine; Computer science; Gerontology; Psychology; Support vector machine; Psychiatry; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007774778,0.0002329572,0.0005311589,0.001052835,0.00007952133,0.00005241793,0.0001312443,0.0001754223,0.0004467082],"category_scores_gemma":[0.000362668,0.000169489,0.0001823571,0.0009607892,0.00006374354,0.00003582979,0.0003463759,0.0009305637,0.00001293477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003263221,"about_ca_system_score_gemma":0.0002205491,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007227344,"about_ca_topic_score_gemma":0.02903883,"domain_scores_codex":[0.9981698,0.00009221159,0.0004617328,0.0005758207,0.0003965952,0.0003037764],"domain_scores_gemma":[0.9989507,0.0001341749,0.0003198559,0.0003687433,0.00009505436,0.0001315393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001074863,0.00006619686,0.9827717,0.001012706,0.001474795,0.00001764768,0.0005897904,0.01345197,0.00004967775,0.000001338296,0.00003150764,0.0004251659],"study_design_scores_gemma":[0.0003894242,0.00008710386,0.9225672,0.0005912813,0.00190694,7.080434e-7,0.00005894653,0.07392894,0.0001087248,0.00003446641,0.0001831828,0.0001431134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967666,0.0006209058,0.00002357926,0.00009804399,0.0003268788,0.000383263,0.00006042135,0.00007475186,0.001645512],"genre_scores_gemma":[0.9922004,0.00005975669,0.0002258921,0.0000303294,0.0001629109,0.00009703889,0.0004857415,0.00004147803,0.006696498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06047697,"threshold_uncertainty_score":0.9993836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01573647939668681,"score_gpt":0.2832827424868767,"score_spread":0.2675462630901899,"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."}}