{"id":"W2925811623","doi":"10.1186/s13073-019-0619-9","title":"Predispositional genome sequencing in healthy adults: design, participant characteristics, and early outcomes of the PeopleSeq Consortium","year":2019,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Invitae; Cullen Foundation; Broad Institute; National Human Genome Research Institute; Illumina; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Personal genomics; Medicine; Whole genome sequencing; DNA sequencing; Family medicine; Genomic sequencing; Genetic testing; Health care; Cohort; Genome; Genetics; Biology; Internal medicine; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02887691,0.0008441641,0.000575107,0.001389108,0.002798238,0.00143441,0.001063696,0.001509886,0.002460959],"category_scores_gemma":[0.02386942,0.0006771404,0.001372627,0.001391913,0.001268625,0.001426413,0.003907263,0.00119196,0.0005766038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146473,"about_ca_system_score_gemma":0.005620082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01289564,"about_ca_topic_score_gemma":0.01796981,"domain_scores_codex":[0.985548,0.009848779,0.0008363833,0.001246152,0.001352872,0.001167811],"domain_scores_gemma":[0.9860826,0.003054488,0.003819939,0.001490179,0.002868073,0.002684718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007276244,0.004780008,0.9622182,0.0001761836,0.0003122133,0.0003537562,0.004504974,0.000276584,0.0008302915,0.0003199799,0.002938094,0.01601348],"study_design_scores_gemma":[0.002116117,0.01162267,0.9719221,0.0001705004,0.0002913194,0.0004939034,0.005098729,0.0008141342,0.001251987,0.0006189054,0.005483023,0.0001166212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909734,0.0001474708,0.000983118,0.0002527967,0.00003229004,0.005045497,0.001452341,0.00002386221,0.001089253],"genre_scores_gemma":[0.9559592,0.0001412631,0.006598331,0.0005887154,0.00007231105,0.03318191,0.002303359,0.00003290045,0.001121892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02887691,"threshold_uncertainty_score":0.1527175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01828430128174426,"score_gpt":0.2460200179016503,"score_spread":0.227735716619906,"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."}}