{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.014439,0.0004938363,0.0008833526,0.0009236733,0.007387123,0.009034817,0.00211351,0.05062409,0.0107396],"category_scores_gemma":[0.05250154,0.0005057857,0.0008652919,0.001186162,0.01225098,0.01744142,0.006828969,0.04397599,0.004337905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006992188,"about_ca_system_score_gemma":0.0144493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0113486,"about_ca_topic_score_gemma":0.02088754,"domain_scores_codex":[0.9883034,0.005812429,0.0007427343,0.0007418292,0.003349506,0.001050155],"domain_scores_gemma":[0.937011,0.04769902,0.001581076,0.001965802,0.005004218,0.00673881],"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":[0.00002027932,0.00003888125,0.0007817406,0.000080624,0.000006682431,0.0008838953,0.001088643,0.0000432642,0.0001099252,0.03076492,0.9302646,0.03591661],"study_design_scores_gemma":[0.00005844262,0.00004533525,0.001081175,0.0009467808,0.00001588072,0.002516786,0.003378692,0.0004113256,0.0001955428,0.09591335,0.8953869,0.00004978874],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001391047,0.0009386669,0.00005376792,0.9958107,0.001083149,0.000002602444,0.000005693479,0.000004000975,0.001962238],"genre_scores_gemma":[0.007760151,0.003705354,0.0003969244,0.9745839,0.008356353,0.00002950363,0.00001705664,0.00001889423,0.00513182],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9978865,"threshold_uncertainty_score":0.07636166,"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."}}