{"id":"W3089760233","doi":"10.1101/2020.09.29.20201632","title":"Artificial intelligence to predict the risk of mortality from Covid-19: Insights from a Canadian Application","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Public health; Agency (philosophy); Psychological intervention; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Demography; Computer science; Medicine; Geography; Disease; Sociology; Pathology; Social 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004708536,0.0003773535,0.0007373592,0.0002264341,0.0001577461,0.00005215342,0.0008295498,0.0003900417,0.0002249001],"category_scores_gemma":[0.005438358,0.000302427,0.0002487646,0.0005036297,0.0002053204,0.00002733131,0.0004725628,0.001037369,0.000165887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007613012,"about_ca_system_score_gemma":0.003454392,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8348177,"about_ca_topic_score_gemma":0.5576,"domain_scores_codex":[0.9966663,0.0003334958,0.0008701546,0.001147432,0.0006893752,0.0002933175],"domain_scores_gemma":[0.9948217,0.001172495,0.0004621564,0.0020251,0.0001902066,0.001328363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005045858,0.0005190668,0.9213002,0.0006888532,0.001859669,0.0002342546,0.02007632,0.01578917,0.005973982,0.001403204,0.01152609,0.02012457],"study_design_scores_gemma":[0.0001797343,0.0001514176,0.7547235,0.0005515589,0.002210884,0.00000122716,0.0004361581,0.03027241,0.01975238,0.1024076,0.08863355,0.0006796145],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.860806,0.0003406669,0.05458643,0.07908155,0.0005754618,0.002150617,0.002255438,0.0001433714,0.00006046207],"genre_scores_gemma":[0.9699125,0.0001237472,0.0008410394,0.02709259,0.0008991904,0.0003931037,0.0006836402,0.0000501487,0.000004045287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2772177,"threshold_uncertainty_score":0.9999428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07675630875430656,"score_gpt":0.3482884182871616,"score_spread":0.271532109532855,"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."}}