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Overall, the count of diagnoses had the best predictive performance for hospital utilization while the Elixhauser index, followed by the Charlson index, had the best predictive performance for mortality.
The model containing the Elixhauser index had the best predictive performance for all outcomes except for hospitalization for any reason, where number of diagnoses performed well.
ScO2 after all influential factors (anesthesia induction, cement implantation and tourniquet deflation) had the best predictive performance for POCD AUCC = 0.742), and the optimal threshold was 66.5 %.
As this model uses all the observations up to 12 weeks collected for both MPG and HbA1c, it is not surprising that this model has the best predictive performance for end-of-trial HbA1c.
In our preliminary evaluation, we found that there is a range of values for R%cutoff in which all these measures were able to achieve statistical significance (Fig. 2) and that a 30%% R%cutoff had the best predictive performance for our study population.
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All models were shown to achieve outstanding predictive performance, the lowest being NS2 model with 96.57% accuracy (AUC = 0.980; MCC = 0.916), while HA prediction model achieved the best predictive performance of 98.62% accuracy (AUC = 0.998; MCC = 0.972).
The best predictive performances of the models were assessed by means of various descriptive statistical indicators.
Italic values denote the best predictive performance among the techniques for each data set.
As ScO2 after all influential factors had best predictive performance, its optimal threshold for POCD detection was determined.
Response curves (after consideration of the effects of other variables in the model) give account of how variables entered ENMs for best predictive performance; they do not represent tests of a priori hypotheses.
Since multiple classification trees, which correspond to alternative ways of partitioning the data set, are considered, each of them is competing for the best predictive performance.
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