{"id":"W4403070547","doi":"10.1093/clinchem/hvae106.497","title":"B-136 From Result to Response: The Development of Laboratory-Based Scoring Models to Predict COVID-19 Patient Outcomes","year":2024,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York Central Hospital; Mount Sinai Hospital; William Osler Health System; Simon Fraser University; University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Medicine; Virology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.002456528,0.0001838656,0.000310238,0.00004128629,0.0001335926,0.0001042201,0.001254232,0.0001490823,0.00002531657],"category_scores_gemma":[0.006229605,0.0001355521,0.0001074832,0.0005389065,0.00004933457,0.0000699411,0.0006110597,0.0005508637,0.00004175792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002640761,"about_ca_system_score_gemma":0.002508537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004357459,"about_ca_topic_score_gemma":0.0000104395,"domain_scores_codex":[0.9969836,0.000313699,0.001087226,0.0007595388,0.0005664998,0.0002894297],"domain_scores_gemma":[0.993771,0.004179368,0.0001312748,0.001163149,0.000152558,0.0006026257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004144846,0.001165852,0.4638743,0.003810314,0.0006235169,0.0004208352,0.05611334,0.1953194,0.01490233,0.001370332,0.06285574,0.1953992],"study_design_scores_gemma":[0.002156924,0.000754284,0.04996074,0.002145823,0.00005621901,0.000004050751,0.0006656064,0.4414834,0.09385363,0.002225717,0.4049601,0.001733535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8659962,0.0001058241,0.09800214,0.03467408,0.0005037109,0.0002704014,0.00007893653,0.0002845274,0.00008417969],"genre_scores_gemma":[0.9318488,8.770099e-7,0.0619236,0.005885648,0.00009328004,0.00007115804,0.00000857264,0.00001812033,0.0001499918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4139135,"threshold_uncertainty_score":0.7457868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010909474756419,"score_gpt":0.4143294170616783,"score_spread":0.3132384695860363,"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."}}