{"id":"W3119890139","doi":"10.1016/j.artmed.2021.102018","title":"A novel computational method for assigning weights of importance to symptoms of COVID-19 patients","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; Sunnybrook Health Science Centre; SickKids Foundation; St Joseph's Health Centre","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Virology; Medicine; Disease","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":[0.001634824,0.0007259695,0.0008902221,0.002820708,0.0007983494,0.001693948,0.001991275,0.001352167,0.004745407],"category_scores_gemma":[0.009157427,0.0004056022,0.0008004718,0.00219358,0.0004773622,0.001116435,0.001138966,0.001277727,0.0005553016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000925559,"about_ca_system_score_gemma":0.002597655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01232097,"about_ca_topic_score_gemma":0.01789762,"domain_scores_codex":[0.9991614,0.0002103767,0.00007179029,0.0001970493,0.000286646,0.00007267811],"domain_scores_gemma":[0.9964272,0.002244893,0.0002014601,0.0002050953,0.0007811415,0.000140217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005100758,0.000568453,0.009383107,0.0002590362,0.0002661748,0.0001931014,0.0002720796,0.2526125,0.00554641,0.01580558,0.01520456,0.699379],"study_design_scores_gemma":[0.00003696151,0.00002462989,0.000525747,0.00001381342,0.00002853363,0.00004496202,0.0000266177,0.992036,0.0005406679,0.005899717,0.0008142055,0.000008126895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02617536,0.0002113243,0.9677265,0.0008941183,0.0001718408,0.0002191442,0.0004773288,0.001110661,0.00301371],"genre_scores_gemma":[0.2532923,0.000129297,0.7416192,0.0003560348,0.000200523,0.0003134157,0.0007444208,0.00007521779,0.00326963],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01232097,"threshold_uncertainty_score":0.02449852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1061794715195817,"score_gpt":0.4389035814378345,"score_spread":0.3327241099182528,"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."}}