{"id":"W3206828823","doi":"10.2196/34082","title":"Peer Review of “Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study”","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Triage; Observational study; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Retrospective cohort study; Artificial intelligence; Medical emergency; Machine learning; Computer science; Data science; Emergency medicine; Virology; Internal medicine; Disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02438956,0.0004776669,0.0014279,0.002226408,0.002478966,0.003095212,0.002224026,0.002183005,0.04582189],"category_scores_gemma":[0.2509174,0.0003792444,0.00167229,0.001728631,0.001594664,0.00210309,0.001896116,0.002226857,0.01583566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312813,"about_ca_system_score_gemma":0.008244831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003341338,"about_ca_topic_score_gemma":0.003309755,"domain_scores_codex":[0.9789916,0.00767117,0.005027344,0.001560321,0.005900579,0.0008490539],"domain_scores_gemma":[0.6580621,0.04403801,0.01384334,0.02248386,0.2526227,0.008949933],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001334326,0.0001727153,0.0275384,0.002652194,0.0004537908,0.0008058967,0.0004844642,0.0001643132,0.0009779122,0.001080664,0.8968083,0.06752691],"study_design_scores_gemma":[0.001300719,0.0006863649,0.09314881,0.005726555,0.0005863036,0.001970994,0.001720476,0.004393871,0.004036879,0.004209121,0.8819942,0.000225804],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.1883972,0.02030021,0.03708163,0.2734968,0.2969604,0.0200084,0.0769399,0.003624326,0.08319105],"genre_scores_gemma":[0.6452919,0.0117573,0.03298388,0.05955226,0.06758601,0.007968199,0.0590284,0.003207683,0.1126243],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9756104,"threshold_uncertainty_score":0.1532894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1526922091680102,"score_gpt":0.3891959051611288,"score_spread":0.2365036959931186,"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."}}