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The summary of sensitivity and specificity was calculated and four rSEPs (rOmpA-2, rOmpB, rRpsB, and rSdhB) had relatively higher scores.
The ROC curve is a plot of true positives (sensitivity) against false positives (1-specificity) that provides a summary of sensitivity and specificity across a range of cut points for a continuous predictor [ 36].
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*The summary of sensitivities of each protein to both acute- and convalescent-phase sera and specificity of each protein was calculated to evaluate its ability as a candidate antigen for diagnosis of FESF.
Also the summary of sensitivities of each protein to both acute- and convalescent-phase sera and specificity of each protein was calculated to generally evaluate its ability as a candidate antigen for diagnosis of FESF.
Our findings about reporting of summary accuracy measures in meta-analyses are different to those reported previously.[ 3] We found a higher rate of use of summary ROC, though use of independent summaries of sensitivity, specificity and predictive values were similar.
We performed a bivariate meta-analysis using a linear mixed model approach to calculate summary estimates of sensitivity, specificity, positive likelihood ratio and negative likelihood ratio, and to fit a hierarchical summary receiver-operating characteristic (HSROC) curve.
The summary receiver operator characteristic (SROC) curve that plotted sensitivity versus specificity was constructed to plot the individual and summary points of sensitivity and specificity [ 32].
Individual and summary estimates of sensitivity and specificity for all studies, the 95% confidence region and 95% prediction region are presented in the summary ROC graph.
The 95% credible region around the summary value of sensitivity and specificity is relatively wide.
Based on an investigation of heterogeneity, summary estimates of sensitivity, specificity and likelihood ratios (LRs) were derived as appropriate.
A cross is used to show the summary estimate of sensitivity and specificity.
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