PMC:7160614 / 24451-25067 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"307","span":{"begin":523,"end":531},"obj":"Disease"}],"attributes":[{"id":"A307","pred":"tao:has_database_id","subj":"307","obj":"MESH:C000657245"}],"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":"Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)"}

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T56","span":{"begin":69,"end":73},"obj":"Body_part"}],"attributes":[{"id":"A56","pred":"fma_id","subj":"T56","obj":"http://purl.org/sig/ont/fma/fma12520"}],"text":"Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)"}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T93","span":{"begin":523,"end":531},"obj":"Disease"}],"attributes":[{"id":"A93","pred":"mondo_id","subj":"T93","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)"}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T149","span":{"begin":246,"end":247},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)"}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T16","span":{"begin":434,"end":445},"obj":"Chemical"}],"attributes":[{"id":"A16","pred":"chebi_id","subj":"T16","obj":"http://purl.obolibrary.org/obo/CHEBI_33232"}],"text":"Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)"}

{"project":"LitCovid-sentences","denotations":[{"id":"T276","span":{"begin":63,"end":357},"obj":"Sentence"},{"id":"T277","span":{"begin":358,"end":616},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)"}