{"id":"W2755356818","doi":"10.17269/cjph.108.5929","title":"Inequalities in oral health: Understanding the contributions of education and income","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Inequality; Logistic regression; Economic inequality; Population; Income distribution; Ethnic group; Demography; Demographic economics; Medicine; Economics; Sociology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002414816,0.000392295,0.000787741,0.001806641,0.001158471,0.002993883,0.001056537,0.001505991,0.004682822],"category_scores_gemma":[0.0127825,0.0004076291,0.0008737106,0.002277326,0.002264296,0.002852527,0.002739145,0.002184341,0.0001529346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002376063,"about_ca_system_score_gemma":0.003666933,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2098417,"about_ca_topic_score_gemma":0.2376087,"domain_scores_codex":[0.9972404,0.0009239194,0.0001087742,0.0002824623,0.0003488382,0.00109566],"domain_scores_gemma":[0.9922518,0.005143759,0.001091957,0.0002904806,0.000432928,0.0007889451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001963516,0.0003298233,0.9144499,0.0001620345,0.0004557599,0.0002082301,0.003505528,0.001038896,0.0001564914,0.03062966,0.001671025,0.04719629],"study_design_scores_gemma":[0.00001574443,0.00005025504,0.9525363,0.0003583876,0.0003287364,0.0001056701,0.00594893,0.002396559,0.00008424914,0.03400705,0.004133824,0.00003420837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9238057,0.01857989,0.00177519,0.03570717,0.0001980411,0.00003052995,0.001229204,0.00001178116,0.01866269],"genre_scores_gemma":[0.9959189,0.002836731,0.0002301152,0.0003871903,0.0001057103,0.000008844265,0.0001222905,0.000003553244,0.0003867607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7901583,"threshold_uncertainty_score":0.4172406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1573930788231649,"score_gpt":0.4057491969306583,"score_spread":0.2483561181074934,"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."}}