Summary of evidence on prevention and management of bladder dysfunction in patients after radical hysterectomy.

AIM: To retrieve, analyse and summarize the relevant evidence on the prevention and management of bladder dysfunction in patients with cervical ancer after radical hysterectomy. DESIGN: Overview of systematic reviews. METHODS: 11 databases were searched for relevant studies from top to bottom according to the '6S' model of evidence-based resources. Two independent reviewers selected the articles, extracted the data and appraised the quality of the included reviews based on different types of evaluation tools. RESULTS: A total of 13 studies were identified, including four clinical consultants, four guidelines, four systematic reviews and one randomized controlled trial. 29 best evidence were summarized from five aspects, including definition, risk factors, assessment, prevention and management.

Examining the Social Networks Types and Their Effects on Caregiving Experience of Family Caregivers for Individuals With Dementia: A Mixed-Methods Study.

Background and Objectives: Social networks are crucial to personal health, particularly among caregivers of individuals with dementia; however, different types of social networks among caregivers of those with dementia and how these differences are associated with caregiver burden and positive appraisal, remain underexamined. This study aims to depict dementia caregivers' social network types, related factors, and impact on caregiving experiences. Research Design and Methods: A questionnaire-based survey was conducted with a total of 237 family caregivers of individuals with dementia nested additional semistructured interviews conducted with 14 caregivers in Chongqing, China. A quantitative study was designed to collect data on personal and situational information, social networks, caregiver burden, and positive aspects of caregiving. Qualitative data were collected via semistructured interviews. Latent class analysis and multivariate regression analyses were applied to quantitative data, and inductive content analysis to qualitative data. Results: The 3 social network types-family-limited (n = 39, 16.46%), family-dominant (n = 99, 41.77%), and diverse network (n = 99, 41.77%)-differed in age and sex of caregivers and individuals with dementia, stage of dementia, and caregiving intensity. Caregivers in family-dominant networks had a lower caregiver burden (ß= -0.299, p = .003) and greater positive aspects of caregiving (ß= 0.228, p = .021) than those in family-limited networks. Three themes-accessibility, reciprocity, and reliance-emerged as facilitators and barriers when asking for support. Caregivers frequently cited the perception of economic, practical, and emotional support, yet reported a lack of adequate formal support from healthcare providers. Discussion and Implication: Family caregivers of individuals with dementia have different social network types that vary considerably among sociocultural contexts and perceive various types of support from social networks. Solid family networks and diverse social networks are contributors to long-term dementia care.

Trends in patient safety education research for healthcare professional students over the past two decades: a bibliometric and content analysis.

Research and practice in patient safety education have garnered widespread attention; however, a comprehensive bibliometric analysis is lacking. This study aimed to provide a comprehensive understanding of the research focus and research trends in the globalization of the field of patient safety education and to describe the general characteristics of publications. Data on articles and reviews about student safety education were extracted from Web of Science. Microsoft Excel 2019, CiteSpace 6.1.R3, VOSviewer 1.6.18, SATI 3.2, Scimago Graphica, and Pajek were used for quantitative analysis. Collaboration networks of countries, institutions, journals, authors, and keywords were visualized based on publications from January 2000 to September 2022. A total of 573 papers were published between 2000 to 2022, showing an overall increasing trend. The USA, England, and Australia are the top three most prolific countries; Johns Hopkins University, the University of Technology Sydney, and the University of Toronto are the top three most productive institutions; Nurse Education Today, Journal of Nursing Education, and BMC Medical Education are the most productive journals; Based on content analysis five research hotspots focused on: (1) Quality Improvement of Patient safety Teaching and Learning; (2) Patient safety Teaching Content; (3)Specialized Teaching in Patient Safety; (4) Integrating Patient Safety and Clinical Teaching; (5)Patient Safety Teaching Assessment Content. Through keyword clustering analysis, five research hotspots and relevant contents were identified. According to this study, simulation, communication, collaboration, and medication may attract more attention from researchers and educators, and could be the major trend for future study.

Three-Dimensional (3D) deep learning model complements existing models for preoperative disease-free survival prediction (DFS) in localized clear cell renal cell carcinoma (ccRCC): A multicenter retrospective cohort study.

BACKGROUND: Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is therefore crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. This purpose of this study was to develop and externally validate a CT-based deep learning (DL) model for pre-surgical disease-free survival (DFS) prediction. METHODS: Patients with localized ccRCC were retrospectively enrolled from six independent medical centers. Three-dimensional (3D) tumor regions from CT images were utilized as input to architect a ResNet 50 model, which outputted DL computed risk score (DLCR) of each patient for DFS prediction later. The predictive performance of DLCR was assessed and compared to the radiomics model (Rad-Score), clinical model we built and two existing prognostic models (UISS and Leibovich). The complementary value of DLCR to the UISS, Leibovich, as well as Rad-Score were evaluated by stratified analysis. RESULTS: 707 patients with localized ccRCC were finally enrolled for models' training and validating. The DLCR we established can perfectly stratify patients into low-, intermediate- and high-risks, and outperformed the Rad-Score, clinical model, UISS and Leibovich score in DFS prediction, with a C-index of 0.754 (0.689-0.821) in the external testing set. Furthermore, the DLCR presented excellent risk stratification capacity in subgroups defined by almost all clinic-pathological features. Moreover, patients in the UISS/Leibovich score/Rad-Score stratified low-risk but DLCR-defined intermediate- and high-risk groups were significantly more likely to experience ccRCC recurrences than those of intermediate- and high-risk in DLCR determined low-risk (all Log-rank P values＜0.05). CONCLUSIONS: Our deep learning model, derived from preoperative CT, is superior to radiomics and current models in precisely DFS predicting of localized ccRCC, and can provide complementary values to them, which may assist more informed clinical decisions and adjuvant therapies adoptions.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

The Application of ChatGPT in Medicine: A Scoping Review and Bibliometric Analysis.

Purpose: ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine. Methods: This scope follows Arksey and O'Malley's five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis. Results: Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism. Conclusion: The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.

An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study.

Background: The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. Methods: In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation. ProCaLNMD was trained at a dataset from a single centre (the Sun Yat-sen Memorial Hospital of Sun Yat-sen University [SYSMH]), and externally validated in the other four centres. A bladder cancer dataset from SYSMH was used to further validate ProCaLNMD, and an additional validation (human-AI comparison and collaboration study) containing consecutive patients with PCa from SYSMH was implemented to evaluate the application value of integrating ProCaLNMD into the clinical workflow. The primary endpoint was the area under the receiver operating characteristic curve (AUROC) of ProCaLNMD. In addition, the performance measures for pathologists with ProCaLNMD assistance was also assessed. Findings: In total, 8225 slides from 1297 patients with PCa were collected and digitised. Overall, 8158 slides (18,761 lymph nodes) from 1297 patients with PCa (median age 68 years [interquartile range 64-73]; 331 [26%] with LNM) were used to train and validate ProCaLNMD. The AUROC of ProCaLNMD ranged from 0.975 (95% confidence interval 0.953-0.998) to 0.992 (0.982-1.000) in the training and validation datasets, with sensitivities > 0.955 and specificities > 0.921. ProCaLNMD also demonstrated an AUROC of 0.979 in the cross-cancer dataset. ProCaLNMD use triggered true reclassification in 43 (4.3%) slides in which micrometastatic tumour regions were initially missed by pathologists, thereby correcting 28 (8.5%) missed-diagnosed cases of previous routine pathological reports. In the human-AI comparison and collaboration study, the sensitivity of ProCaLNMD (0.983 [0.908-1.000]) surpassed that of two junior pathologists (0.862 [0.746-0.939], P = 0.023; 0.879 [0.767-0.950], P = 0.041) by 10-12% and showed no difference to that of two senior pathologists (both 0.983 [0.908-1.000], both P > 0.99). Furthermore, ProCaLNMD significantly boosted the diagnostic sensitivity of two junior pathologists (both P = 0.041) to the level of senior pathologists (both P > 0.99), and substantially reduced the four pathologists' slide reviewing time (-31%, P < 0.0001; -34%, P < 0.0001; -29%, P < 0.0001; and -27%, P = 0.00031). Interpretation: ProCaLNMD demonstrated high diagnostic capabilities for identifying LNM in prostate cancer, reducing the likelihood of missed diagnoses by pathologists and decreasing the slide reviewing time, highlighting its potential for clinical application. Funding: National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer.

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.

BACKGROUND: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. METHODS: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. RESULTS: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. CONCLUSIONS: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

Facilitators and barriers to the implementation of dietary nutrition interventions for community-dwelling older adults with physical frailty and sarcopenia: A qualitative meta-synthesis.

Objectives: With the acceleration of an aging society, the prevalence of age-related chronic diseases such as physical frailty and sarcopenia is gradually increasing with numerous adverse effects. Dietary nutrition is an important modifiable risk factor for the management of physical frailty and sarcopenia, but there are many complex influences on its implementation in community settings. This study aimed to summarize the facilitators and barriers to the implementation of dietary nutrition interventions for community-dwelling older adults with physical frailty and sarcopenia, and to provide a reference for the formulation of relevant health management programs. Methods: Searches were conducted in databases including PubMed, Web of Science, Medline (Ovid), Embase (Ovid), and Cochrane Library from inception to January 2023. Searches were completed for a combination of MeSH terms and free terms. The Critical Appraisal Skills Program (CASP) instrument was used to appraise quality. Coding and analysis of the extracted information were performed using the socio-ecological modeling framework. The study protocol for this review was registered on the PROSPERO ( CRD42022381339). Results: A total of 10 studies were included. Of these, four were nutrition-only focused interventions, and six were dietary nutrition and exercise interventions. The facilitators and barriers were summarized based on the socio-ecological model that emerged at three levels: individual trait level, external environment level, and intervention-related level, containing ten subthemes. Conclusion: Individual internal motivation and external support should be integrated with the implementation of diet- and nutrition-related interventions in community-living aged people with physical frailty and sarcopenia. Develop "tailored" interventions for participants and maximize available human and physical resources.

Contribution of rumination and psychological resilience to post-traumatic growth of front-line healthcare workers in mobile cabin hospitals under Normalized epidemic Prevention and Control Requirements.

To understand the level of post-traumatic growth (PTG) and influencing factors among front-line healthcare workers (HCWs) working in mobile cabin hospitals treating patients with Coronavirus Disease 2019 (COVID-19) under the Normalized Epidemic Prevention and Control Requirements adopted in China. A random sampling method was used to select 540 HCWs of the Chongqing-aid-Shanghai medical team from April to May 2022 as the study participants. Participants completed a general information questionnaire, the Post-traumatic Growth Inventory-Chinese version (PTGI-C), the Chinese version of the Connor-Davidson Resilience Scale (CD-RISC) and the Chinese Event Related Rumination Inventory (C-ERRI). Among the 540 included HCWs, 83.15 % were nurses and 78.89 % were women. The average scores for PTG (62.25 ± 16.73) and psychological resilience (64.22 ± 15.38) were at moderate levels, and the average score for rumination was low (21.62 ± 10.77). Pearson correlation analysis showed that CD-RISC and C-ERRI scores were positive with the PTGI-C score (r = 0.528, 0.316, P < 0.001). Multiple linear regression analysis identified psychological training or intervention during the COVID-19 epidemic (ß = 2.353, P = 0.044), psychological resilience (ß = 0.525, P < 0.001) and deliberate rumination (ß = 0.732, P < 0.001) as factors significantly associated with the PTG of front-line HCWs, which together explained 36.8 % of the total variance in PTG (F[5,539] = 63.866, P < 0.001). In general, psychological resilience and deliberate rumination can promote PTG among HCWs and can be improved by strengthening psychological training and interventions for HCWs working under the Normalized Epidemic Prevention and Control Requirements.

Driving the implementation of hospital examination reservation system through hospital management.

BACKGROUND: Hospital Examination Reservation System (HERS) was designed for reducing appointment examination waiting time and enhancing patients' medical satisfaction in China, but implementing HERS would encounter many difficulties. This study would investigate the factors that influence patients' utilization of HERS through UTAUT2, and provide valuable insights for hospital managements to drive the effective implementation of HERS. It is helpful for improving patients' medical satisfaction. METHODS: We conducted a survey through the Sojump platform, targeting patients were who have already used HERS. We collected questionnaire information related to factors behavior intention, performance expectancy, and effort expectancy. Subsequently, we employed a structural equation model to analyze the factors influencing patients' utilization of HERS. RESULTS: A total of 394 valid questionnaires were collected. Habit was the main direct positive factor influencing the behavioral intention of HERS (ß = 0.593; 95%CI: 0.072, 1.944; P = 0.002), followed by patient innovation (ß = 0.269; 95%CI: 0.002, 0.443; P < 0.001), effort expectancy (ß = 0.239; 95%CI: -0.022, 0.478; P = 0.048). Patient innovation and facilitating conditions also have an indirect effect on behavioral intention. Perceived privacy exposure has a significantly negative effect on behavioral intention (ß=-0.138; 95%CI: -0.225, -0.047; P < 0.001). The above variables explained 56.7% of the variation in behavioral intention. CONCLUSIONS: When HERS is implemented in hospitals, managements should arrange volunteers to guide patients to bring up the habit and solve the using difficulties, and managements could invite patients with high innovation to recommend HERS to others, what's more, it is a valid way to retain the old form of appointment to pass the transition period to the new system. HERS utilization and patients' medical satisfaction will be enhanced through the guidance of hospital management means.

Level and influencing factors of transition shock among new nurses in China: A multicenter cross-sectional study.

Background and Aims: New nurses are an important part of nursing teams. The failure of new nurses to successfully transition seriously affects personal career development and nursing work quality, and important influencing factors deserve the attention of nursing managers. At present, multicenter, large-sample investigations of transition shock among new nurses are lacking in China. This study aims to investigate the current level and influencing factors of transition shock among new nurses in China. Methods: We conducted a multicenter, cross-sectional study with 3414 new nurses from 16 provinces in 7 regions in China from October 22, 2021, to November 8, 2021. We used the snowball sampling method and an online questionnaire produced by the researchers to collect data; the questionnaire included questions on demographic information, a transition shock scale for new nurses and open-ended questions. Data were analyzed using SPSS version 24. Results: The effective response rate of this study was 97.89%, with 3342 effective participants from 189 hospitals in China, most of whom were female (94.88%). The study showed that the transition shock of new nurses in China was at a moderate level, with pre-job anxiety, unsatisfactory welfare treatment, resignation intention, adverse events, poor sleep quality, 1 or fewer exercise sessions per week, inability to balance work and life, and gluttony negatively affecting the transition shock of new nurses in China. Psychological shock was the strongest among the four dimensions of transition shock. Conclusions: The transition shock of new nurses, especially their psychological shock, deserves more attention from international society. Nursing managers should continue to take supportive measures to intervene in the factors influencing transition shock, with the aim of reducing the level of transition for new nurses, promoting their personal thriving, improving the quality of nursing work and increasing the retention rate of nurses.

Effectiveness of the Stress Process Model-Based Program in Dementia Caregiving (DeCare-SPM) for Family Caregivers: A Study Protocol for a Randomized Controlled Trial.

This paper aims to describe a randomized controlled trial protocol evaluating the effectiveness, cost, and process of a stress process model-based program in dementia caregiving (DeCare-SPM) for family caregivers. Family caregivers of individuals with dementia will be recruited from memory clinics and community settings and randomly assigned to either DeCare-SPM or usual care. DeCare-SPM comprises three face-to-face sessions (ie, problem-based coping, emotion-based coping, meaning-based coping), and a fourth session (ie, social support) including weekly telephone-based consultation for four weeks and then monthly face-to-face boosters. Outcomes will be measured at baseline (T0), and at one (T1), three (T2), and six months (T3). The primary outcome is positive aspects of caregiving and secondary outcomes are caregiving (ie, sense of competence, caregiver burden, social support, anxiety, depression, and quality of life), dementia-related (ie, care dependency, neuropsychiatric symptoms, and quality of life), and stress-related biomarkers of blood and saliva. In addition, process and economic evaluations will be performed. Mixed-effects models will be used to assess intervention effects. Content analysis will be performed on the qualitative data. This paper described the protocol for comprehensive evaluation of the effectiveness, cost, and process of the theory-driven DeCare-SPM to inform how and why interventions work. It highlights the need to reduce challenges and enhance the positive aspects of dementia care. The DeCare-SPM will provide evidence-based insights into how to support and empower family caregivers in their important roles, thereby, leading to improved dementia care.

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study.

OBJECTIVE: To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). METHODS: A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809-0.914), 0.853 (95% CI: 0.785-0.921), and 0.837 (95% CI: 0.714-0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495-8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118-149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821-0.923), 0.865 (95% CI: 0.800-0.930), and 0.848 (95% CI: 0.728-0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups. CONCLUSIONS: The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients. CRITICAL RELEVANCE STATEMENT: The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC. KEY POINTS: • The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC.

Analysis of influencing factors of job demands of healthcare workers working in mobile cabin hospitals in China.

AIM: To explore the job demands of healthcare workers (HCWs) working in mobile cabin hospitals in Shanghai and identify the influencing factors. DESIGN: The study had a cross-sectional design. METHODS: Using the convenience sampling method, we selected 1223 HCWs (medical team members) working in these mobile cabin hospitals during April-May 2022. The findings of the general information questionnaire and the hierarchy scale of job demands of HCWs working in mobile cabin hospitals were used for the investigation. RESULTS: The total score of job demands of the included HCWs was 132.26 ± 9.53; the average score of the items was 4.73 ± 0.34. Multivariate linear regression analyses showed that the following HCWs had significantly higher job demands: female HCWs and HCWs who received psychological training or intervention during the COVID-19 pandemic, were satisfied with the doctor/nurse-patient relationship, received support from family members/friends/colleagues, believed that the risk of working in mobile cabin hospitals was high, had adapted to the working environment of mobile cabin hospitals and had college/undergraduate level of education. They would benefit from increased social support and better training in terms of psychological coping mechanisms(both theoretical knowledge and applicable skills) and COVID-19 prevention,control and treatment abilities.

Latent class analysis of healthcare workers' perceptions of workers' job demands in mobile cabin hospitals in China.

BACKGROUND: Owing to the outbreak of the Omicron variant of SARS-CoV-2 in Shanghai, China, partitioned dynamic closure and control management plans were implemented on March 28, 2022. This created huge emergency pressure on Shanghai's medical and healthcare systems. However, the perceptions of job demands of healthcare workers (HCWs) and classification of frontline HCWs in mobile cabin hospitals are unknown. METHODS: In this study, we investigated the job demands of 1223 frontline HCWs working in mobile cabin hospitals during the COVID-19 pandemic April 2022 to May 2022. We performed latent class analysis to identify classification features of job demands. A binary multivariate logistic regression model was used to explore the influencing factors of latent class. RESULTS: The total mean job demand score was 132.26 (SD = 9.53), indicating a high level of job demand. A two-class model provided the best fit. The two classes were titled "middle-demand group" (17.66%) and "high-demand group" (82.34%). A regression analysis suggested that female HCWs, HCWs satisfied with the doctor/nurse-patient relationship, HCWs who believed that the risk of working in mobile cabin hospitals was high, and HCWs without physical discomfort during the pandemic were more likely to be in the "high-demand group". CONCLUSION: Characteristics of the "high-demand group" subtype suggest that attention should be paid to the physical condition of frontline HCWs and the job demands of female HCWs. Managers should strengthen the training of HCWs in terms of their communication skills as well as their knowledge and technical skills to aid epidemic prevention and control.

Acquisition behaviours for nutrition-related information based on a health promotion model for older adults in a long-term care facility.

AIM: To explore the acquisition behaviours for nutrition-related information of older adults in a long-term care facility. DESIGN: A qualitative descriptive design was used in this study. METHODS: Sixteen older adults in a long-term care facility were recruited using purposive sampling between March and May 2021. Data were collected via face-to-face semi-structured interviews, based on open questions regarding acquisition behaviours for nutrition-related information and flexible question formulation, and the data were analysed using an inductive-deductive method. A health promotion model was used as a conceptual framework to regulate the refinement of themes. RESULTS: Three themes were identified in this study. The first theme discussed the individual characteristics and experiences of older adults that contributed to their acquisition behaviours for nutrition-related information. The second theme described behaviour-specific cognitions of and the effects on the participants regarding the influencing factors involving various internal individual elements and external physical environment. The third theme explored the positive behavioural outcomes of the participants resulting from these acquisition behaviours. CONCLUSION: Acquisition behaviours for nutrition-related information of older adults in long-term care facilities were affected by both individual characteristics and external physical environment factors. Access to nutritional information can help older adults cultivate a healthy diet. Although they exhibited a significant interest in nutrition, the participants still encountered several difficulties. Based on the actual care needs of the older people, appropriate nutritional information interventions should be provided by healthcare providers working in long-term care facilities so as to improve the ability of the older people to acquire information independently. PATIENT OR PUBLIC CONTRIBUTION: All 16 participants actively participated in the interview process and the preliminary preparation of the article.