{"id":"W4391529487","doi":"10.1101/2024.02.03.24302302","title":"Predicting Diabetes in Canadian Adults Using Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Ted Rogers Centre for Heart Research; York University; Public Health Ontario; University of Toronto","funders":"","keywords":"Diabetes mellitus; Machine learning; Computer science; Artificial intelligence; Psychology; Medicine; Endocrinology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001394885,0.0006879007,0.0003598646,0.001596358,0.000760771,0.0009215368,0.0009546016,0.0004074111,0.002900102],"category_scores_gemma":[0.00507298,0.0001822646,0.0005982233,0.002120812,0.000207706,0.0002487112,0.000433156,0.0006225769,0.0006341418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01078597,"about_ca_system_score_gemma":0.01444865,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9801777,"about_ca_topic_score_gemma":0.9701378,"domain_scores_codex":[0.9994747,0.00008210656,0.00002848415,0.00011789,0.0001883602,0.0001085162],"domain_scores_gemma":[0.9982369,0.0004178543,0.0001666136,0.00006794398,0.0009378894,0.000172877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003044747,0.0001776599,0.8640833,0.00018032,0.0002116611,0.00009746208,0.0001764761,0.03818155,0.0004997075,0.0009408251,0.02204837,0.07309818],"study_design_scores_gemma":[0.00008059248,0.0001134959,0.5161089,0.0001458359,0.0001362528,0.0001089751,0.0005462311,0.4700851,0.001372082,0.0009341762,0.01031261,0.00005561011],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9342442,0.001562798,0.008356495,0.003545576,0.0001298634,0.0002059745,0.04199126,0.0006293719,0.009334519],"genre_scores_gemma":[0.9722214,0.0005931691,0.01014523,0.0002864029,0.00003530506,0.00004277751,0.01446075,0.0000177525,0.002197136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0198223,"threshold_uncertainty_score":0.07825804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.123155462629746,"score_gpt":0.4421076639942231,"score_spread":0.3189522013644772,"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."}}