Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer-The BRAIN Study.

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2- breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2- breast cancer.

[The Influence of Grit on Turnover Intention of University Hospital Nurses: The Mediating Effect of Job Involvement].

PURPOSE: This study aimed to confirm the mediating effect of job involvement in the relationship between grit and turnover intention among nurses working at university hospitals. METHODS: Participants included 437 nurses from university hospitals located in C city, Gyeongnam. Data were collected from January 8 to 19, 2018, using self-report questionnaires. Data were analyzed using the t-test, analysis of variance, Scheffe's test, Pearson's correlation coefficient, and multiple regression, with the SPSS/22.0 program. A mediation analysis was performed according to the Baron and Kenny, and bootstrapping methods. RESULTS: There were significant relationships between grit and job involvement (r=.40, p<.001), grit and turnover intention (r=-.29, p<.001), and turnover intention and job involvement (r=-.52, p<.001). Job involvement showed partial mediating effects in the relationship between grit and turnover intention. CONCLUSION: Grit increased job involvement and lowered turnover intention. Therefore, to reduce nurses' turnover intention, it is necessary to develop a program and strategies to increase their grit.

[Structural Equation Model for Sleep Quality of Female Shift Work Nurses].

PURPOSE: This study aimed to develop and test a structural model for sleep quality in female shift work nurses. The hypothetical model was constructed on the basis of Spielman's 3P model of insomnia and previous research related to the sleep quality of shift nurses. METHODS: This cross-sectional study used structural equation modeling and recruited 285 female shift work nurses from four general and university hospitals with over 300 beds located in C and J cities in Gyeongsangnamdo. Data were collected from September 27 to October 20, 2016, and then analyzed using descriptive statistics, Pearson's correlation, and structural equation modeling. The study used SPSS/Win 18.0 and AMOS 18.0 in processing the data. RESULTS: The final model showed good fit to the empirical data: χ²/df=2.19, SRMR=.07, RMSEA=.07, AGFI=.85, TLI=.91, GFI=.93, GFI=.89, NFI=.87. The factors that influenced sleep quality were sleep hygiene (ß=.32), perceived shift work status (ß=-.16), stress response (ß=.16), shift work experience (ß=.15), perceived health status (ß=-.14), and circadian rhythm (ß=-.13) explaining 36.0% of the variance. CONCLUSION: The model of sleep quality of the shift work nurses constructed in this study is recommended as a model to understand and predict the sleep quality of shift work nurses. The results suggest that strategies for improving the sleep quality of shift work nurses should focus on sleep hygiene, perceived health status, stress response, circadian rhythm, perceived shift work status, and shift work experience.