{"id":"W2770167700","doi":"10.1590/0102-311x00172316","title":"Income inequality and high blood pressure in Colombia: a multilevel analysis","year":2017,"lang":"en","type":"article","venue":"Cadernos de Saúde Pública","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fulbright Colombia; International Development Research Centre; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Inequality; Blood pressure; Economic inequality; Demography; Logistic regression; Medicine; Multilevel model; Income distribution; Gini coefficient; Demographic economics; Economics; Sociology; Internal medicine; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0006866015,0.0003645053,0.000601448,0.001506835,0.0009999397,0.001197068,0.0004776701,0.0002913234,0.002857156],"category_scores_gemma":[0.00217538,0.0002370085,0.000956472,0.001706928,0.0002806149,0.000386495,0.001490692,0.0006246637,0.0001162727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002161065,"about_ca_system_score_gemma":0.001490795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3521414,"about_ca_topic_score_gemma":0.3843511,"domain_scores_codex":[0.9992193,0.0003197172,0.00004148073,0.0001320779,0.00008516701,0.000202318],"domain_scores_gemma":[0.9990773,0.0002479243,0.0002905267,0.0000882458,0.0001386016,0.0001574325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001054513,0.00006289894,0.9941156,0.00004472188,0.0003387328,0.00009093418,0.0004442573,0.0003687501,0.0001127455,0.0002595548,0.0003990517,0.00365724],"study_design_scores_gemma":[0.0000136026,0.0000406576,0.9947023,0.00005540154,0.0002067451,0.00005109809,0.001195874,0.002941034,0.00003997164,0.0001646992,0.0005755478,0.0000131391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996336,0.0005879993,0.000467382,0.0003124611,0.000009310596,0.00004223779,0.001166316,0.00001086928,0.001067397],"genre_scores_gemma":[0.9986789,0.0001695827,0.0004793637,0.00001901877,0.000005003618,0.00002820827,0.0004540206,0.000002655836,0.0001633168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3521414,"threshold_uncertainty_score":0.7001835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04216989581066809,"score_gpt":0.3677991222295221,"score_spread":0.325629226418854,"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."}}