{"id":"W4406353319","doi":"10.2139/ssrn.5095147","title":"Predicting Multimorbidity in 50,000 People Living with Obesity: A Machine Learning Model Applied to Understand Obesity Progression in Two Health Care Systems","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vale (Canada)","funders":"","keywords":"Obesity; Multimorbidity; Health care; Gerontology; Medicine; Computer science; Psychology; Comorbidity; Psychiatry; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003055643,0.0005598659,0.0007047172,0.001091143,0.0005720957,0.001306667,0.0009603817,0.001395722,0.001693915],"category_scores_gemma":[0.009505303,0.000408642,0.00117067,0.0009947072,0.0003416199,0.00109047,0.001050597,0.001426053,0.0003317571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142241,"about_ca_system_score_gemma":0.001154294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07226392,"about_ca_topic_score_gemma":0.04862188,"domain_scores_codex":[0.9993069,0.0002786977,0.00005703666,0.0001881331,0.00005496562,0.0001142542],"domain_scores_gemma":[0.99541,0.003093702,0.0004360885,0.0002632358,0.0004094997,0.000387414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005373585,0.0006482885,0.9491662,0.00002384905,0.0003093111,0.0001155321,0.0001932476,0.0404271,0.0001561875,0.0002885831,0.001053386,0.007080866],"study_design_scores_gemma":[0.00008490954,0.0002750854,0.4306886,0.0000195351,0.0001573292,0.00008448846,0.0004991646,0.5667735,0.0001736122,0.0009576587,0.0002560274,0.00003001357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974508,0.00005378733,0.001138021,0.0003149509,0.00001705718,0.00001761713,0.0008279904,0.0000196095,0.0001602328],"genre_scores_gemma":[0.9969272,0.00003710869,0.001247033,0.00003651746,0.00001455425,0.0000198837,0.001417276,0.000005003247,0.0002954527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07226392,"threshold_uncertainty_score":0.1436866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02062823335717134,"score_gpt":0.3261387971800482,"score_spread":0.3055105638228769,"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."}}