{"id":"W2930790887","doi":"10.1017/s0021932019000178","title":"Ethnicity-specific cut-offs that predict co-morbidities: the way forward for optimal utility of obesity indicators","year":2019,"lang":"en","type":"letter","venue":"Journal of Biosocial Science","topic":"Obesity and Health Practices","field":"Health Professions","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ethnic group; Obesity; Medicine; Adipose tissue; Gerontology; Internal medicine; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0128465,0.0007186804,0.002004274,0.001816016,0.001680577,0.003163294,0.001513596,0.01578632,0.003591515],"category_scores_gemma":[0.06967527,0.0004158978,0.001215013,0.001361227,0.002394618,0.004498087,0.001389684,0.02045848,0.00317862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003625428,"about_ca_system_score_gemma":0.004235588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005843192,"about_ca_topic_score_gemma":0.008199702,"domain_scores_codex":[0.9917089,0.004238372,0.001520952,0.0005395955,0.001510697,0.0004814346],"domain_scores_gemma":[0.9512652,0.03086565,0.001693027,0.00121167,0.01227752,0.002686866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006292553,0.0004318555,0.06341001,0.0003709566,0.00008673596,0.003193,0.0009647122,0.0005780106,0.000963788,0.007775821,0.7203417,0.2012541],"study_design_scores_gemma":[0.001151977,0.001025146,0.0963014,0.007783065,0.0002691758,0.01924568,0.009071606,0.01465525,0.002100439,0.1505962,0.6971042,0.0006957973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002629134,0.002700176,0.0005518746,0.9871216,0.004906903,0.00002075694,0.00007206998,0.00002454879,0.001972869],"genre_scores_gemma":[0.06390925,0.006203642,0.01201143,0.8663553,0.0459193,0.0003622345,0.0003180795,0.00007749606,0.004843389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01578632,"threshold_uncertainty_score":0.06793958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217867314825274,"score_gpt":0.4455031268205245,"score_spread":0.323716395337997,"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."}}