Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach’s α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach’s α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Background: s/Aims: Systematic investigations into the prognostic impact of the longitudinal tumor location in gallbladder cancer (GBC) remain insufficient. To address the limitations of our pilot study, we conducted a multicenter investigation to clarify the impact of the longitudinal tumor location on the oncological outcomes of GBC. Methods: A retrospective multicenter study was conducted on 372 patients undergoing radical resections for GBC from January 2010 to December 2019 across seven hospitals that belong to the Daejeon–Chungcheong branch of the Korean Association of Hepato-Biliary-Pancreatic Surgery. Patients were divided into GBC in the fundus/body (FB-GBC) and GBC in the neck/cystic duct (NC-GBC) groups, based on the longitudinal tumor location. Results: Of 372 patients, 282 had FB-GBC, while 90 had NC-GBC. NC-GBC was associated with more frequent elevation of preoperative carbohydrate antigen (CA) 19-9 levels, requirement for more extensive surgery, more advanced histologic grade and tumor stages, more frequent lymphovascular and perineural invasion, lower R0 resection rates, higher recurrence rates, and worse 5-year overall and disease-free survival rates. Propensity score matching analysis confirmed these findings, showing lower R0 resection rates, higher recurrence rates, and worse survival rates in the NC-GBC group. Multivariate analysis identified elevated preoperative CA 19-9 levels, lymph node metastasis, and non-R0 resection as independent prognostic factors, but not longitudinal tumor location. Conclusions NC-GBC exhibits more frequent elevation of preoperative CA 19-9 levels, more advanced histologic grade and tumor stages, lower R0 resection rates, and poorer overall and disease-free survival rates, compared to FB-GBC. However, the longitudinal tumor location was not analyzed as an independent prognostic factor.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: s/Aims: Systematic investigations into the prognostic impact of the longitudinal tumor location in gallbladder cancer (GBC) remain insufficient. To address the limitations of our pilot study, we conducted a multicenter investigation to clarify the impact of the longitudinal tumor location on the oncological outcomes of GBC. Methods: A retrospective multicenter study was conducted on 372 patients undergoing radical resections for GBC from January 2010 to December 2019 across seven hospitals that belong to the Daejeon–Chungcheong branch of the Korean Association of Hepato-Biliary-Pancreatic Surgery. Patients were divided into GBC in the fundus/body (FB-GBC) and GBC in the neck/cystic duct (NC-GBC) groups, based on the longitudinal tumor location. Results: Of 372 patients, 282 had FB-GBC, while 90 had NC-GBC. NC-GBC was associated with more frequent elevation of preoperative carbohydrate antigen (CA) 19-9 levels, requirement for more extensive surgery, more advanced histologic grade and tumor stages, more frequent lymphovascular and perineural invasion, lower R0 resection rates, higher recurrence rates, and worse 5-year overall and disease-free survival rates. Propensity score matching analysis confirmed these findings, showing lower R0 resection rates, higher recurrence rates, and worse survival rates in the NC-GBC group. Multivariate analysis identified elevated preoperative CA 19-9 levels, lymph node metastasis, and non-R0 resection as independent prognostic factors, but not longitudinal tumor location. Conclusions NC-GBC exhibits more frequent elevation of preoperative CA 19-9 levels, more advanced histologic grade and tumor stages, lower R0 resection rates, and poorer overall and disease-free survival rates, compared to FB-GBC. However, the longitudinal tumor location was not analyzed as an independent prognostic factor.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: s/Aims: Systematic investigations into the prognostic impact of the longitudinal tumor location in gallbladder cancer (GBC) remain insufficient. To address the limitations of our pilot study, we conducted a multicenter investigation to clarify the impact of the longitudinal tumor location on the oncological outcomes of GBC. Methods: A retrospective multicenter study was conducted on 372 patients undergoing radical resections for GBC from January 2010 to December 2019 across seven hospitals that belong to the Daejeon–Chungcheong branch of the Korean Association of Hepato-Biliary-Pancreatic Surgery. Patients were divided into GBC in the fundus/body (FB-GBC) and GBC in the neck/cystic duct (NC-GBC) groups, based on the longitudinal tumor location. Results: Of 372 patients, 282 had FB-GBC, while 90 had NC-GBC. NC-GBC was associated with more frequent elevation of preoperative carbohydrate antigen (CA) 19-9 levels, requirement for more extensive surgery, more advanced histologic grade and tumor stages, more frequent lymphovascular and perineural invasion, lower R0 resection rates, higher recurrence rates, and worse 5-year overall and disease-free survival rates. Propensity score matching analysis confirmed these findings, showing lower R0 resection rates, higher recurrence rates, and worse survival rates in the NC-GBC group. Multivariate analysis identified elevated preoperative CA 19-9 levels, lymph node metastasis, and non-R0 resection as independent prognostic factors, but not longitudinal tumor location. Conclusions NC-GBC exhibits more frequent elevation of preoperative CA 19-9 levels, more advanced histologic grade and tumor stages, lower R0 resection rates, and poorer overall and disease-free survival rates, compared to FB-GBC. However, the longitudinal tumor location was not analyzed as an independent prognostic factor.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.