{"id":"W4285677475","doi":"10.1002/gepi.22492","title":"Including diverse and admixed populations in genetic epidemiology research","year":2022,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"National Institute of Child Health and Human Development; Australian Research Council; National Cancer Institute; National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Achievement Rewards for College Scientists Foundation; Canada Research Chairs; Deutsche Forschungsgemeinschaft; National Institute of Mental Health; University of North Texas","keywords":"Genetic epidemiology; Interpretation (philosophy); Epidemiology; Population; Public health; Biology; Genetics; Sociology; Medicine; Demography; Pathology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01073898,0.0003322827,0.0009186447,0.0004845234,0.0009100401,0.000005527566,0.0005646693,0.0004402873,0.0002303289],"category_scores_gemma":[0.01163891,0.0003614342,0.0001628511,0.0005321373,0.0005466786,0.000004573828,0.001801301,0.0008318576,0.00002897036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001779115,"about_ca_system_score_gemma":0.0002038128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001123799,"about_ca_topic_score_gemma":0.0008867224,"domain_scores_codex":[0.9850133,0.01036612,0.001509381,0.001358166,0.0001803359,0.00157272],"domain_scores_gemma":[0.9961766,0.002177414,0.0004038866,0.0008221665,0.0001308549,0.000289089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006611345,0.00008493588,0.9597797,0.00001244736,0.00004603849,0.00001310137,0.0001190113,0.01746598,0.001844686,0.002113396,0.007465752,0.01098879],"study_design_scores_gemma":[0.0007760157,0.0006775517,0.9264002,0.000006169208,0.00002272834,0.0001389952,0.000601274,0.003857126,0.00001639559,0.04494549,0.02220692,0.0003510709],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782224,0.009732782,0.006100693,0.004158166,0.0006006432,0.0006041348,0.00005741234,0.00002521578,0.000498577],"genre_scores_gemma":[0.9403265,0.001489252,0.05412816,0.002428232,0.0003543244,0.0005140646,0.0002157159,0.00004839239,0.0004953921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04802747,"threshold_uncertainty_score":0.9998838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.304862031725472,"score_gpt":0.449353443862178,"score_spread":0.144491412136706,"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."}}