{"id":"W3082407621","doi":"10.1101/2020.08.26.20182840","title":"On Statistical Power for Case-Control Host Genomic Studies of COVID-19","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Waterloo; Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Jewish General Hospital; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"University of Toronto; Innovation, Science and Economic Development Canada; Genome Canada","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Genetic predisposition; Genetic variation; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Biology; Infectivity; Host (biology); Disease; 2019-20 coronavirus outbreak; Computational biology; Virology; Infectious disease (medical specialty); Medicine; Genetics; Virus; Outbreak; Gene; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.4899338,0.00157967,0.004612857,0.003685888,0.001851991,0.006342103,0.004641996,0.005617767,0.0159733],"category_scores_gemma":[0.7488394,0.001208324,0.007044894,0.004175713,0.008648126,0.006629522,0.005689089,0.00509814,0.001686633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001425888,"about_ca_system_score_gemma":0.002216719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651388,"about_ca_topic_score_gemma":0.0006303317,"domain_scores_codex":[0.3911538,0.5490313,0.01214935,0.02138137,0.02355007,0.002734124],"domain_scores_gemma":[0.1219773,0.8152568,0.01560536,0.0404025,0.005421011,0.001336855],"domain_codex":"methods","domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0327966,0.001388783,0.327342,0.008496387,0.04567691,0.003504737,0.005404791,0.0404287,0.005708303,0.2698044,0.02627327,0.2331751],"study_design_scores_gemma":[0.009560407,0.01330158,0.1639878,0.004938168,0.01520345,0.004112933,0.003017013,0.273551,0.009425093,0.4244322,0.07791876,0.0005516279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1732931,0.01288999,0.7537469,0.01363779,0.004543974,0.005990779,0.005450481,0.001248755,0.02919806],"genre_scores_gemma":[0.9232677,0.0008192012,0.06321936,0.002427361,0.0006253032,0.006001832,0.001379545,0.0003059444,0.001953738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4899338,"threshold_uncertainty_score":0.6290025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113597797017106,"score_gpt":0.4231422407404225,"score_spread":0.3117824610387119,"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."}}