{"id":"W2793863401","doi":"10.1136/bmjopen-2017-018680","title":"Model-based recursive partitioning to identify risk clusters for metabolic syndrome and its components: findings from the International Mobility in Aging Study","year":2018,"lang":"en","type":"article","venue":"BMJ Open","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institute on Minority Health and Health Disparities; Canadian Institutes of Health Research; Universidade Federal do Rio Grande do Norte; Universidad de Caldas; Queen's University","keywords":"Recursive partitioning; Medicine; Gerontology; Cluster (spacecraft); Metabolic syndrome; Epidemiology; Demography; Longitudinal study; Life course approach; Obesity; Psychology; Developmental psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008516073,0.0008550338,0.0009124968,0.00125123,0.0009370449,0.001061814,0.001243411,0.0004959818,0.001355223],"category_scores_gemma":[0.03179373,0.0003931963,0.002333855,0.001674966,0.0005698655,0.000829194,0.001578908,0.00107062,0.000279849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008522479,"about_ca_system_score_gemma":0.001698037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04340893,"about_ca_topic_score_gemma":0.04997848,"domain_scores_codex":[0.995608,0.003315895,0.0001298604,0.000413485,0.0003492975,0.0001834699],"domain_scores_gemma":[0.988091,0.007587307,0.001249014,0.001277005,0.001340578,0.0004550969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004218336,0.0002283896,0.9566187,0.0001633738,0.001608088,0.0001369449,0.001898066,0.00652955,0.0002031572,0.00109332,0.002344999,0.02875355],"study_design_scores_gemma":[0.0001239305,0.0004155432,0.8938682,0.0002090559,0.0009218965,0.0003261228,0.002156806,0.09195594,0.0001783078,0.007641234,0.002148288,0.00005461053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708317,0.001450876,0.02386948,0.001194924,0.00004419788,0.0002578196,0.001075736,0.0001057324,0.001169499],"genre_scores_gemma":[0.9884564,0.0002160455,0.009700364,0.00008929786,0.00001603954,0.0001600391,0.001013452,0.00003146943,0.0003168688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04340893,"threshold_uncertainty_score":0.08631253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1535788677686479,"score_gpt":0.4773049999262679,"score_spread":0.32372613215762,"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."}}