PMC:7386785 / 40755-42380 JSONTXT

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T66813","span":{"begin":402,"end":403},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T45238","span":{"begin":689,"end":690},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T38494","span":{"begin":762,"end":771},"obj":"http://purl.obolibrary.org/obo/OBI_0000245"},{"id":"T48854","span":{"begin":874,"end":875},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T92010","span":{"begin":1001,"end":1002},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T25863","span":{"begin":1115,"end":1120},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T13001","span":{"begin":1396,"end":1400},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T91491","span":{"begin":1419,"end":1424},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T60880","span":{"begin":1492,"end":1495},"obj":"http://purl.obolibrary.org/obo/CLO_0051568"},{"id":"T22863","span":{"begin":1497,"end":1500},"obj":"http://purl.obolibrary.org/obo/CLO_0051568"},{"id":"T12395","span":{"begin":1582,"end":1586},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"}],"text":"Statistical analysis and data synthesis\nWe present results of estimated sensitivity and specificity using paired forest plots and summarised in tables as appropriate.\nWe present the results without meta‐analysis, due to the small numbers of studies currently available, considerable heterogeneity across studies and the high risk of bias that we identified, as we felt doing so would otherwise produce a seemingly more accurate estimate than the underlying evidence is able to provide at this moment in time.\nWe present results of estimated sensitivity and specificity using paired forest plots in Review Manager 2014, and dumbbell plots to display the change in disease probability after a positive or negative result.\nWe disaggregated data by study design and organised by target condition, reporting results from cross‐sectional studies separately from studies that used a diagnostic case‐control or other design that we assessed as prone to high risk of bias.\nWhen pooling does become possible in a future update of this review, we will estimate mean sensitivity and specificity using hierarchical models where tests either report binary results or at commonly reported thresholds. Where data are sparse, we will use methods described by Takwoingi 2017 for obtaining estimates from simplified models. We anticipate that over time sufficient data will accumulate to provide clear estimates of test accuracy for some tests. We will undertake meta‐analysis in STATA version 16.0 (STATA), or SAS (SAS 2015), as detailed in the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Chapter 10; Macaskill 2013)."}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T25","span":{"begin":30,"end":39},"obj":"http://purl.obolibrary.org/obo/GO_0009058"}],"text":"Statistical analysis and data synthesis\nWe present results of estimated sensitivity and specificity using paired forest plots and summarised in tables as appropriate.\nWe present the results without meta‐analysis, due to the small numbers of studies currently available, considerable heterogeneity across studies and the high risk of bias that we identified, as we felt doing so would otherwise produce a seemingly more accurate estimate than the underlying evidence is able to provide at this moment in time.\nWe present results of estimated sensitivity and specificity using paired forest plots in Review Manager 2014, and dumbbell plots to display the change in disease probability after a positive or negative result.\nWe disaggregated data by study design and organised by target condition, reporting results from cross‐sectional studies separately from studies that used a diagnostic case‐control or other design that we assessed as prone to high risk of bias.\nWhen pooling does become possible in a future update of this review, we will estimate mean sensitivity and specificity using hierarchical models where tests either report binary results or at commonly reported thresholds. Where data are sparse, we will use methods described by Takwoingi 2017 for obtaining estimates from simplified models. We anticipate that over time sufficient data will accumulate to provide clear estimates of test accuracy for some tests. We will undertake meta‐analysis in STATA version 16.0 (STATA), or SAS (SAS 2015), as detailed in the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Chapter 10; Macaskill 2013)."}

{"project":"LitCovid-sentences","denotations":[{"id":"T397","span":{"begin":0,"end":39},"obj":"Sentence"},{"id":"T398","span":{"begin":40,"end":166},"obj":"Sentence"},{"id":"T399","span":{"begin":167,"end":508},"obj":"Sentence"},{"id":"T400","span":{"begin":509,"end":719},"obj":"Sentence"},{"id":"T401","span":{"begin":720,"end":963},"obj":"Sentence"},{"id":"T402","span":{"begin":964,"end":1185},"obj":"Sentence"},{"id":"T403","span":{"begin":1186,"end":1304},"obj":"Sentence"},{"id":"T404","span":{"begin":1305,"end":1425},"obj":"Sentence"},{"id":"T405","span":{"begin":1426,"end":1625},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Statistical analysis and data synthesis\nWe present results of estimated sensitivity and specificity using paired forest plots and summarised in tables as appropriate.\nWe present the results without meta‐analysis, due to the small numbers of studies currently available, considerable heterogeneity across studies and the high risk of bias that we identified, as we felt doing so would otherwise produce a seemingly more accurate estimate than the underlying evidence is able to provide at this moment in time.\nWe present results of estimated sensitivity and specificity using paired forest plots in Review Manager 2014, and dumbbell plots to display the change in disease probability after a positive or negative result.\nWe disaggregated data by study design and organised by target condition, reporting results from cross‐sectional studies separately from studies that used a diagnostic case‐control or other design that we assessed as prone to high risk of bias.\nWhen pooling does become possible in a future update of this review, we will estimate mean sensitivity and specificity using hierarchical models where tests either report binary results or at commonly reported thresholds. Where data are sparse, we will use methods described by Takwoingi 2017 for obtaining estimates from simplified models. We anticipate that over time sufficient data will accumulate to provide clear estimates of test accuracy for some tests. We will undertake meta‐analysis in STATA version 16.0 (STATA), or SAS (SAS 2015), as detailed in the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Chapter 10; Macaskill 2013)."}

{"project":"LitCovid-PubTator","denotations":[{"id":"718","span":{"begin":1492,"end":1495},"obj":"Gene"},{"id":"719","span":{"begin":1497,"end":1500},"obj":"Gene"}],"attributes":[{"id":"A718","pred":"tao:has_database_id","subj":"718","obj":"Gene:6302"},{"id":"A719","pred":"tao:has_database_id","subj":"719","obj":"Gene:6302"}],"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":"Statistical analysis and data synthesis\nWe present results of estimated sensitivity and specificity using paired forest plots and summarised in tables as appropriate.\nWe present the results without meta‐analysis, due to the small numbers of studies currently available, considerable heterogeneity across studies and the high risk of bias that we identified, as we felt doing so would otherwise produce a seemingly more accurate estimate than the underlying evidence is able to provide at this moment in time.\nWe present results of estimated sensitivity and specificity using paired forest plots in Review Manager 2014, and dumbbell plots to display the change in disease probability after a positive or negative result.\nWe disaggregated data by study design and organised by target condition, reporting results from cross‐sectional studies separately from studies that used a diagnostic case‐control or other design that we assessed as prone to high risk of bias.\nWhen pooling does become possible in a future update of this review, we will estimate mean sensitivity and specificity using hierarchical models where tests either report binary results or at commonly reported thresholds. Where data are sparse, we will use methods described by Takwoingi 2017 for obtaining estimates from simplified models. We anticipate that over time sufficient data will accumulate to provide clear estimates of test accuracy for some tests. We will undertake meta‐analysis in STATA version 16.0 (STATA), or SAS (SAS 2015), as detailed in the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Chapter 10; Macaskill 2013)."}