{"id":"W3042334944","doi":"10.1007/s12630-020-01767-5","title":"Re-visiting preoperative SARS-CoV-2 testing using a Bayesian approach","year":2020,"lang":"en","type":"letter","venue":"Canadian Journal of Anesthesia/Journal canadien d anesthésie","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"St Mary's Hospital Centre; Jewish General Hospital","funders":"","keywords":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Bayesian probability; Virology; Medicine; Computer science; Artificial intelligence; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002070219,0.0003882711,0.0007115558,0.001164273,0.001065693,0.001655481,0.0009987549,0.005523832,0.008272016],"category_scores_gemma":[0.03487593,0.000507312,0.001203265,0.0004415199,0.0006590042,0.001724816,0.0009322832,0.008644865,0.002033064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002038241,"about_ca_system_score_gemma":0.003705948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01100953,"about_ca_topic_score_gemma":0.02445119,"domain_scores_codex":[0.9982627,0.0007577391,0.0002392712,0.0001826658,0.000330583,0.0002270046],"domain_scores_gemma":[0.9854865,0.01062173,0.0004708892,0.0004619285,0.001893479,0.001065432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006310918,0.0005081283,0.07311869,0.0002765858,0.0002024623,0.03394021,0.000641911,0.01039575,0.00152506,0.01716675,0.5835407,0.2780526],"study_design_scores_gemma":[0.0008596052,0.001085394,0.06058992,0.004065492,0.0005842805,0.08833271,0.003017946,0.1598369,0.002233058,0.3154659,0.3635758,0.0003527901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.03342782,0.003747389,0.0415167,0.8503196,0.006355826,0.0003002533,0.0006868293,0.0003315351,0.0633141],"genre_scores_gemma":[0.554158,0.005631746,0.0648629,0.3232387,0.03478043,0.0003559903,0.0009574274,0.0002955786,0.01571932],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01100953,"threshold_uncertainty_score":0.02767265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08564402156910263,"score_gpt":0.3144619065397632,"score_spread":0.2288178849706606,"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."}}