{"id":"W4303423400","doi":"10.1002/cpz1.534","title":"Genome Reporting for Healthy Populations—Pipeline for Genomic Screening from the GENCOV COVID‐19 Study","year":2022,"lang":"en","type":"article","venue":"Current Protocols","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Hospital for Sick Children; University Health Network; University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Canadian Institutes of Health Research","keywords":"Genotyping; Context (archaeology); Disease; Medicine; Personalized medicine; Bioinformatics; Genetics; Biology; Internal medicine; Genotype; 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":[0.01823551,0.001881237,0.002004187,0.009487339,0.0008578774,0.005003212,0.003065366,0.001503316,0.09888887],"category_scores_gemma":[0.0613234,0.001864146,0.001832069,0.009070694,0.0003946469,0.00252013,0.005992641,0.002395798,0.06417192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001599469,"about_ca_system_score_gemma":0.004876443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01284296,"about_ca_topic_score_gemma":0.007893997,"domain_scores_codex":[0.989817,0.00319712,0.002532769,0.001453451,0.002560232,0.0004393649],"domain_scores_gemma":[0.9557784,0.01455576,0.005666302,0.00829455,0.0125883,0.003116683],"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":[0.0009693587,0.0001185207,0.01611834,0.0008469848,0.0001921629,0.0004506052,0.000323147,0.000805493,0.001384617,0.002600987,0.857814,0.1183759],"study_design_scores_gemma":[0.001211,0.0003105108,0.05651538,0.001038072,0.0002232606,0.0008318028,0.0004638211,0.007075555,0.006213969,0.01409166,0.9117152,0.0003096068],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.005934534,0.0009773538,0.07043795,0.006196268,0.0007546864,0.006253552,0.8256678,0.05681941,0.02695856],"genre_scores_gemma":[0.01719995,0.00134778,0.1793791,0.0026585,0.0007662821,0.01102474,0.7643711,0.0122639,0.01098863],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.09888887,"threshold_uncertainty_score":0.3308163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1982293026120375,"score_gpt":0.4468443088788523,"score_spread":0.2486150062668148,"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."}}