{"id":"W2014968248","doi":"10.1161/circulationaha.106.635011","title":"Defining Obesity Cut Points in a Multiethnic Population","year":2007,"lang":"en","type":"article","venue":"Circulation","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":559,"is_retracted":false,"has_abstract":true,"ca_institutions":"Assembly of First Nations; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Body mass index; Obesity; Demography; Ethnic group; Risk factor; Population; Type 2 diabetes; Chinese people; Diabetes mellitus; Internal medicine; Gerontology; China; Endocrinology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031651,0.0001046051,0.0002482466,0.0002245964,0.00005093675,0.00001175235,0.00003319365,0.0001317391,0.00002619301],"category_scores_gemma":[0.0002529504,0.0001119457,0.0001498325,0.0003137602,0.00001671497,0.0001409168,0.00001366809,0.0001584225,0.00007705806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002004357,"about_ca_system_score_gemma":0.00002363284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003517393,"about_ca_topic_score_gemma":0.002144949,"domain_scores_codex":[0.9988838,0.00004719007,0.0002939682,0.0002348741,0.0002999311,0.0002402743],"domain_scores_gemma":[0.9994853,0.00004112956,0.00007124351,0.0002624949,0.00005905921,0.00008082225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003096828,0.00006562159,0.8126171,0.00004322261,0.00002508144,0.00001638717,0.0002840198,0.0002348413,0.0002146721,0.0001329518,0.000008266381,0.1863269],"study_design_scores_gemma":[0.001269026,0.00001782671,0.9958426,0.0001081303,0.00005181543,0.0000162851,0.00005770969,0.001327293,0.0005118119,0.0004970729,0.0001859874,0.0001144515],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930726,0.0006362168,0.004044697,0.00004696584,0.0001703092,0.0004242107,9.006031e-7,0.00008212471,0.001522022],"genre_scores_gemma":[0.9984003,0.00000734752,0.001141453,0.0001164167,0.0001462527,0.000008008592,0.0001356527,0.00002001305,0.00002450642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1862125,"threshold_uncertainty_score":0.5317269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576642287026206,"score_gpt":0.2719529133655358,"score_spread":0.2561864904952738,"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."}}