{"id":"W2077528165","doi":"10.1186/1471-2458-8-66","title":"Monitoring trends in socioeconomic health inequalities: it matters how you measure","year":2008,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Biostatistics; Socioeconomic status; Medicine; Binomial regression; Logistic regression; Demography; Odds ratio; Social class; Inequality; Epidemiology; Public health; Confidence interval; Odds; Environmental health; Population; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006336614,0.0002176357,0.0006301418,0.0004614154,0.001493802,0.0001784217,0.0004131406,0.0001654189,0.0003083001],"category_scores_gemma":[0.0002873804,0.0002319831,0.0001340757,0.0005255379,0.0002196182,0.0006990008,0.00005028171,0.0003909868,0.00005631521],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003399421,"about_ca_system_score_gemma":0.008548454,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1119743,"about_ca_topic_score_gemma":0.05673571,"domain_scores_codex":[0.9947155,0.001374965,0.0007940363,0.0004366596,0.0005733174,0.002105524],"domain_scores_gemma":[0.9976816,0.0003133598,0.0003476424,0.0003167405,0.0000654425,0.001275273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006577616,0.00008660895,0.7877529,0.0003379538,0.000008990241,0.000003211352,0.09233513,0.000004295057,5.008217e-8,0.007560083,0.08430401,0.02760016],"study_design_scores_gemma":[0.0006194436,0.00005375429,0.4596888,0.00009684235,6.752609e-7,0.000003119314,0.06613727,0.00001564369,2.0392e-7,0.00007620415,0.4730618,0.0002462337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1883348,0.001336786,0.0001783929,0.8046648,0.001414041,0.0003641701,0.0000348156,0.000184493,0.003487697],"genre_scores_gemma":[0.9427823,0.002360973,0.0006623612,0.04769489,0.001036239,0.00007022398,0.00001844631,0.00003625183,0.005338346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7569699,"threshold_uncertainty_score":0.9998061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1821291510512302,"score_gpt":0.3942942792931927,"score_spread":0.2121651282419624,"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."}}