PMC:7116472 / 20165-20662 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"287","span":{"begin":25,"end":34},"obj":"Disease"},{"id":"288","span":{"begin":237,"end":246},"obj":"Disease"},{"id":"289","span":{"begin":383,"end":392},"obj":"Disease"}],"attributes":[{"id":"A287","pred":"tao:has_database_id","subj":"287","obj":"MESH:D003643"},{"id":"A288","pred":"tao:has_database_id","subj":"288","obj":"MESH:D003643"},{"id":"A289","pred":"tao:has_database_id","subj":"289","obj":"MESH:D003643"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Discrimination of the 4C Mortality Score in the validation cohort was similar to that of the XGBoost model (table 3). Calibration was also found to be excellent in the validation cohort: overall observed (30.1%) versus predicted (30.1%) mortality was equal (calibration-in-the-large=0) and calibration was excellent over the range of risk (slope=1, Brier score 0.171; fig 2). The 4C Mortality Score showed good performance in clinically relevant metrics across a range of cut-off values (table 4)."}

{"project":"LitCovid-sentences","denotations":[{"id":"T136","span":{"begin":0,"end":117},"obj":"Sentence"},{"id":"T137","span":{"begin":118,"end":375},"obj":"Sentence"},{"id":"T138","span":{"begin":376,"end":497},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Discrimination of the 4C Mortality Score in the validation cohort was similar to that of the XGBoost model (table 3). Calibration was also found to be excellent in the validation cohort: overall observed (30.1%) versus predicted (30.1%) mortality was equal (calibration-in-the-large=0) and calibration was excellent over the range of risk (slope=1, Brier score 0.171; fig 2). The 4C Mortality Score showed good performance in clinically relevant metrics across a range of cut-off values (table 4)."}