Purpose: Health behaviors for metabolic syndrome (MetS) prevention should be emphasized from early adulthood. There is little information on psychosocial factors associated with health behaviors for MetS prevention. The aim of this study was to determine whether there would be a mediating effect of perceived stress on the association between social support and health behaviors for MetS prevention among university students. Methods: This cross-sectional and correlation study was conducted with 502 university students in South Korea. Social support, perceived stress, and lifestyle evaluation for metabolic syndrome scales were used. Online questionnaire survey was conducted between November and December 2019. The mediating effect of social support on health behaviors for MetS prevention was analyzed using PROCESS macro program with bootstrapping method to test our hypotheses. Results: Social support directly influenced perceived stress (β=-.35, p<.001) and health behaviors for MetS prevention (β=.14, p=.002). Health behaviors for MetS prevention was indirectly influenced by perceived stress (β=-.25, p<.001). The size of indirect effect of social support on health behaviors for MetS prevention was 0.06. Conclusions The association of social support and health behaviors for MetS prevention was partially mediated by perceived stress among university students. Therefore, a university-based nursing intervention should comprise social support strategies with stress management to promote health behaviors for MetS prevention.

OBJECTIVE: To evaluate the frequency and adequacy of statistical analyses in a general radiology journal when reporting a reliability analysis for a diagnostic test. MATERIALS AND METHODS: Sixty-three studies of diagnostic test accuracy (DTA) and 36 studies reporting reliability analyses published in the Korean Journal of Radiology between 2012 and 2016 were analyzed. Studies were judged using the methodological guidelines of the Radiological Society of North America-Quantitative Imaging Biomarkers Alliance (RSNA-QIBA), and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative. DTA studies were evaluated by nine editorial board members of the journal. Reliability studies were evaluated by study reviewers experienced with reliability analysis. RESULTS: Thirty-one (49.2%) of the 63 DTA studies did not include a reliability analysis when deemed necessary. Among the 36 reliability studies, proper statistical methods were used in all (5/5) studies dealing with dichotomous/nominal data, 46.7% (7/15) of studies dealing with ordinal data, and 95.2% (20/21) of studies dealing with continuous data. Statistical methods were described in sufficient detail regarding weighted kappa in 28.6% (2/7) of studies and regarding the model and assumptions of intraclass correlation coefficient in 35.3% (6/17) and 29.4% (5/17) of studies, respectively. Reliability parameters were used as if they were agreement parameters in 23.1% (3/13) of studies. Reproducibility and repeatability were used incorrectly in 20% (3/15) of studies. CONCLUSION: Greater attention to the importance of reporting reliability, thorough description of the related statistical methods, efforts not to neglect agreement parameters, and better use of relevant terminology is necessary.
Biomarkers ; Diagnostic Tests, Routine* ; Methods*

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.