Although low-dose hydrocortisone therapy ameliorates septic shock at 7 and 28 days, it does not reduce 28-day mortality.

The incidence of pulmonary complications after abdominal surgery is higher than that of cardiac complications. The perioperative factors currently used to assess the risk of postoperative pulmonary complications (PPCs) are imperfect. FeNO is a marker of respiratory system disease related to the airway inflammatory response and bronchial hyperresponsiveness; it may be a new indicator to screen PPCs. A total of 162 patients over 60 years old scheduled for major abdominal surgery under general anesthesia were chosen to measure their preoperative FeNO level. Statistical analyses including the receiver operating characteristic (ROC) and general linear regression were used to analyze the relationships of FeNO with PPCs and other parameters. The medians and quartiles of preoperative FeNO were 14.33 (9.67-21.10) ppb; the geometric mean was 14.25 ppb. Preoperative FeNO correlated to age (P < 0.05), and the coefficient of association was 0.267. ROC curve analysis of FeNO and PPCs resulted in a high probability with an area under the curve of 0.747 (p = 0.001, 95% confidence interval =0.602-0.893). The cut-off level was 30.2 ppb, with 47.06% sensitivity and 93.10% specificity. The positive predictive value of the cut-off was 42.11% and negative predictive value was 93.70%. OR value was 10.83. The magnitude of FeNO in the PPCs group was larger than that in the non-PPCs groups 26.20 (11.55 - 39.20) versus 13.50 (9.55-20.00); p = 0.008). Preoperative FeNO levels may be used to screen the patients over 60 years old undergoing abdominal surgery with a lower probability to suffer PPCs whoes FeNO values less than 30.2 ppb.

One purpose of cognitive diagnostic model (CDM) is designed to make inferences about unobserved latent classes based on observed item responses. A heuristic for test construction based on the CDM information index (CDI) proposed by Henson and Douglas (2005) has a far-reaching impact, but there are still many shortcomings. He and other researchers had also proposed new methods to improve or overcome the inherent shortcomings of the CDI test assembly method. In this study, one test assembly method of maximizing the minimum inter-class distance is proposed by using mixed-integer linear programming, which aims to overcome the shortcomings that the CDI method is limited to summarize the discriminating power of each item into a single CDI index while neglecting the discriminating power for each pair of latent classes. The simulation results show that compared with the CDI test assembly and random test assembly, the new test assembly method performs well and has the highest accuracy rate in terms of pattern and attributes correct classification rates. Although the accuracy rate of the new method is not very high under item constraints, it is still higher than the CDI test assembly with the same constraints.

The implementation of cognitive diagnostic computerized adaptive testing often depends on a high-quality item bank. How to online estimate the item parameters and calibrate the Q-matrix required by items becomes an important problem in the construction of the high-quality item bank for personalized adaptive learning. The related previous research mainly focused on the calibration method with the random design in which the new items were randomly assigned to examinees. Although the way of randomly assigning new items can ensure the randomness of data sampling, some examinees cannot provide enough information about item parameter estimation or Q-matrix calibration for the new items. In order to increase design efficiency, we investigated three adaptive designs under different practical situations: (a) because the non-parametric classification method needs calibrated item attribute vectors, but not item parameters, the first study focused on an optimal design for the calibration of the Q-matrix of the new items based on Shannon entropy; (b) if the Q-matrix of the new items was specified by subject experts, an optimal design was designed for the estimation of item parameters based on Fisher information; and (c) if the Q-matrix and item parameters are unknown for the new items, we developed a hybrid optimal design for simultaneously estimating them. The simulation results showed that, the adaptive designs are better than the random design with a limited number of examinees in terms of the correct recovery rate of attribute vectors and the precision of item parameters.