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The bounds for this tuning parameter are analyzed using the maximum sensitivity.
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Among the different ways of introducing a robustness constraint into the design stage, the purpose of this paper is to use the maximum sensitivity value as the design parameter.
The transfer-line temperature was set to 280°C and the indicated detector temperature was 172° C. Detection parameters were tuned using the maximum-sensitivity autotune mode.
Using the maximum sum of sensitivity and specificity as a rule to obtain the best cut-off point for classification we arrived at the following results: For 1 year mortality the classification threshold was 0.28 with a sensitivity of 68.8% and specificity of 72.7%.
The optimal cutoffs were determined using the maximum sum of sensitivity and specificity.
Optimal SQR thresholds from the ROC curve were determined using the maximum Youden index (J = sensitivity + specificity − 1).
First we conducted a sensitivity analysis using the maximum number of achievable lives saved as practice performance often exceeded the upper threshold target for maximum payment.
A sensitivity analysis using the maximum score revealed the indices of test accuracy to be similar to those of the original calculation using the minimum score.
Specificity, sensitivity, and accuracy for each contour map were evaluated using the maximum posteriori probability for assignment of an outcome.
The sensitivity of the Temp Diff parameter to prevalence was a function of using the maximum temperature as the response variable.
A sensitivity analysis was conducted to investigate the effect of missing data on the CAST by rerunning the analysis using the maximum score.
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