Interpretable CT Radiomics-based Machine Learning Model for Preoperative Prediction of Ki-67 Expression in Clear Cell Renal Cell Carcinoma.

Rationale And Objectives: To develop and externally validate interpretable CT radiomics-based machine learning (ML) models for preoperative Ki-67 expression prediction in clear cell renal cell carcinoma (ccRCC).

Methods: 506 patients were retrospectively enrolled from three independent institutes and divided into the training (n=357) and external test (n=149) sets. Ki67 expression was determined by immunohistochemistry (IHC) and categorized into low (<15%) and high (≥15%) expression groups.

Return to work in young and middle-aged colorectal cancer survivors: Factors influencing self-efficacy, fear, resilience, and financial toxicity.

Background: Return to work (RTW) serves as an indication for young and middle-aged colorectal cancer (CRC) survivors to resume their normal social lives. However, these survivors encounter significant challenges during their RTW process. Hence, scientific research is necessary to explore the barriers and facilitating factors of returning to work for young and middle-aged CRC survivors.

Facilitators and Barriers of Large Language Model Adoption Among Nursing Students: A Qualitative Descriptive Study.

Aim: To explore nursing students' perceptions and experiences of using large language models and identify the facilitators and barriers by applying the Theory of Planned Behaviour.

Design: A qualitative descriptive design.

Method: Between January and June 2024, we conducted individual semi-structured online interviews with 24 nursing students from 13 medical universities across China.

Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews.

Background: Progress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI's ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the integration of LLMs within mental health practice remain an underreported topic. Therefore, this study aimed to explore how mental health and AI experts conceptualize LLMs and perceive the use of integrating LLMs into mental health practice.

Multiparameter computed tomography (CT) radiomics signature fusion-based model for the preoperative prediction of clear cell renal cell carcinoma nuclear grade: a multicenter development and external validation study.

Background: The preoperative prediction of the pathological nuclear grade of clear cell renal cell carcinoma (CCRCC) is crucial for clinical decision making. However, radiomics features from one or two computed tomography (CT) phases are required to predict the CCRCC grade, which reduces the predictive performance and generalizability of this method. We aimed to develop and externally validate a multiparameter CT radiomics-based model for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of CCRCC.

Integrated multicenter deep learning system for prognostic prediction in bladder cancer.

Precise survival risk stratification is crucial for personalized therapy in bladder cancer (BCa). This study developed and validated an end-to-end deep learning system using histological slides to predict overall survival (OS) risk in BCa patients. We employed the BlaPaSeg tile classifier to generate tissue probability heatmaps and segmentation maps, trained two prognostic networks, MacroVisionNet and UniVisionNet, and explored six potential BCa prognostic biomarkers.

"Learn from Errors": Post-traumatic growth among second victims.

Background: Second victims, defined as healthcare providers enduring emotional and psychological distress after patient safety incidents (PSIs). The potential for positive transformation through these experiences is underexplored but is essential for fostering a culture of error learning and enhancing patient care.

Objective: To explore the level and determinants of post-traumatic growth (PTG), applying the stress process model.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.