{"id":"W4391738016","doi":"10.1101/2024.02.03.24302304","title":"Predicting Time to Diabetes Diagnosis Using Random Survival Forests","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":4,"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":"Random forest; Concordance; Predictive modelling; Machine learning; Medicine; Population; Comorbidity; Computer science; Artificial intelligence; Diabetes mellitus; Internal medicine; Environmental health","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":[0.004605824,0.000634705,0.0005578921,0.001040867,0.0002654276,0.0005855009,0.0006130452,0.0005247672,0.001531779],"category_scores_gemma":[0.009190137,0.0002319612,0.0008268952,0.0005908498,0.0002191211,0.0004026873,0.000321081,0.0007562109,0.0005352872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005535394,"about_ca_system_score_gemma":0.0008957663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02329099,"about_ca_topic_score_gemma":0.02093049,"domain_scores_codex":[0.999206,0.0004313641,0.00003703827,0.0001669338,0.00007140559,0.00008737114],"domain_scores_gemma":[0.9943177,0.004404444,0.0003183142,0.000309813,0.0005021233,0.0001476579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006811006,0.0002136226,0.1318866,0.00006436037,0.0002828906,0.000214631,0.00005519827,0.7774295,0.0007619127,0.001312299,0.005495074,0.08160283],"study_design_scores_gemma":[0.00001861559,0.00004496902,0.003973581,0.00001078525,0.00001936742,0.00003108693,0.000009689119,0.9938489,0.0002697696,0.001518805,0.0002470343,0.000007514762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7073876,0.001175214,0.2814568,0.001214944,0.0002411324,0.0001741598,0.004760779,0.001815067,0.001774378],"genre_scores_gemma":[0.9642575,0.0001407274,0.0322945,0.000106805,0.00007285234,0.00004038966,0.002397573,0.00003236669,0.0006572485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02329099,"threshold_uncertainty_score":0.04631084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230201395628427,"score_gpt":0.2707482927195191,"score_spread":0.2477281531566763,"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."}}