{"id":"W4387965987","doi":"10.1159/000534804","title":"Digital Health Tools in Genomics: Advancing Diversity, Equity, and Inclusion","year":2023,"lang":"en","type":"letter","venue":"Public Health Genomics","topic":"Biotechnology and Related Fields","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Health equity; Equity (law); Genomics; Diversity (politics); Inclusion (mineral); Precision medicine; Data science; Medicine; Computer science; Genome; Biology; Psychology; Public health; Genetics; Sociology; Political science; Nursing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.002233929,0.0003895055,0.001071296,0.000957413,0.001442879,0.000119243,0.0005758559,0.005840568,0.00001896174],"category_scores_gemma":[0.0002718797,0.0003919802,0.0001135789,0.0004905947,0.0001935661,0.0002936577,0.042191,0.0106336,0.00006363457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002658631,"about_ca_system_score_gemma":0.003108151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004663984,"about_ca_topic_score_gemma":0.0001640683,"domain_scores_codex":[0.9962594,0.0001114323,0.0008692269,0.0007685419,0.0004610376,0.001530349],"domain_scores_gemma":[0.998256,0.0002133387,0.0004443418,0.0005578931,0.00006209034,0.0004663902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003020858,0.0000740503,0.0008543381,0.001788621,0.00009797761,0.0003774231,0.003170223,0.000002314635,0.0000059576,0.0001478851,0.5059583,0.4874927],"study_design_scores_gemma":[0.001025675,0.0003520339,0.0007823967,0.0002881708,0.00001297841,0.0001904353,0.0002194974,0.0001297668,0.000001347614,0.001751686,0.9949428,0.0003032159],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003732359,0.003092209,0.0002785694,0.9888361,0.0009006691,0.001107492,0.0001773824,0.000298042,0.001577185],"genre_scores_gemma":[0.004316897,0.02442584,0.0005566503,0.9669614,0.001067981,0.00001450576,0.001346678,0.0001060323,0.001204039],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.4889845,"threshold_uncertainty_score":0.9998571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07365709518358797,"score_gpt":0.3269855771426259,"score_spread":0.253328481959038,"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."}}