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Background: This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence–based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time. Methods: One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole‐slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS‐guided and binary diagnosis, inter‐ and intraobserver agreement, and screening time, were compared across all methods and reviewers. Results: AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%–30.6% to 63.9%), positive predictive value (PPV; from 21.6%–24.3% to 31.1%), and negative predictive value (NPV; from 91.3%–91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high‐grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%–27.3% to 33.3%), PPV (from 31.3%–47.4% to 61.1%), and NPV (from 91.6%–92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%–82.2% to 90.0%) and NPV (from 91.7%–93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57–0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75–0.88). AIxURO significantly reduced screening time by 52.3%–83.2% from microscopy and 43.6%–86.7% from WSI review across all reviewers. Screening‐positive (AUC+) cases required more time than negative cases across all methods and reviewers. Conclusions: AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes. By using The Paris System for Reporting Urinary Cytology criteria, the AIxURO tool increased sensitivity and negative predictive value for atypical urothelial cell–positive (AUC+) bladder cancer cases in urine cytology while reducing screening time. The increase in detected positive (AUC+) cases suggests that further study is needed to address the potential increase in false positives. [ABSTRACT FROM AUTHOR]

The present study applied the social-ecological framework to explore factors associated with adolescents' experiences in bullying victimization in Taiwanese middle schools. Data include a random sample of 1,262 students, ages 13-15, from 43 middle schools in Taiwan's New Taipei City. Hierarchical generalized linear modeling was utilized to analyze the data, which included hierarchical and nested structures, such as individual and area level variables. Descriptive statistics and basic inferential analysis were conducted to test the hypotheses that individual (delinquency), family (parental monitoring), school (teacher support and teacher/staff punishment), community (community environment), and area levels (poverty rate) were associated with bullying victimization. Findings reveal that individual (Grade 7, conduct problems), school (teacher/staff punishment), community environment, and criminal activities had significant effects on bullying victimization in Taiwan. Implications for practice are discussed.

Many medical applications are interested to know the disease status. The disease status can be related to multiple serial measurements. Nevertheless, owing to various reasons, the binary outcome can be measured incorrectly. The estimators derived from the misspecified outcome can be biased. This paper derives the complete data likelihood function to incorporate both the multiple serial measurements and the misspecified outcome. Owing to the latent variables, EM algorithm is used to derive the maximum-likelihood estimators. Monte Carlo simulations are conducted to compare the impact of misspecification on the estimates. A retrospective data for the recurrence of atrial fibrillation is used to illustrate the usage of the proposed model. [ABSTRACT FROM AUTHOR]

Motor impairment evaluations are key rehabilitation-related assessments for patients with stroke. Currently, such evaluations are subjective; they are based on physicians’ judgements regarding the actions performed by patients. This leads to inconsistent clinical results. Many inertial sensing elements for motion detection have been designed. However, to more easily and rapidly evaluate motor impairment, we require a system that can collect data effectively to predict the degree of motor impairment. Lin et al. used data gloves equipped with an inertial measurement unit (IMU) to collect movement trajectories for motor impairment evaluations in patients with stroke. The present study used functional data analysis to model data trajectories to reduce the influence of noise from IMU data and proposed using coefficients of function as features for classifying motor impairment. To verify the appropriateness of feature construction, five classification methods were used to evaluate the extracted features in terms of the overall and sensor-specific ability to classify levels of motor impairment. The results indicated that the features derived from cubic smoothing splines could effectively reflect key data characteristics, and a support vector machine yielded relatively high overall and sensor-specific accuracy for distinguishing between levels of motion impairment in patients with stroke. Future data glove systems can contain cubic smoothing splines to extract hand function features and then classify motion impairment for appropriate rehabilitation programs to be prescribed. [ABSTRACT FROM AUTHOR]