Effectiveness of simulation-based clinical research curriculum for undergraduate medical students - a pre-post intervention study with external control.

BACKGROUND: Simulation is widely utilized in medical education. Exploring the effectiveness of high-fidelity simulation of clinical research within medical education may inform its integration into clinical research training curricula, finally cultivating physician-scientist development. METHODS: Standard teaching scripts for both clinical trial and cross-sectional study simulation were designed. We recruited undergraduates majoring in clinical medicine at 3th grade into a pre-post intervention study. Additionally, a cross-sectional survey randomly selected medical undergraduates at 4th or 5th grade, medical students in master and doctor degree as external controls. Self-assessment scores of knowledge and practice were collected using a 5-point Likert scale. Changes in scores were tested by Wilcoxon signed-rank test and group comparisons were conducted by Dunn's tests with multiple corrections. Multivariable quantile regressions were used to explore factors influencing the changes from baseline. RESULTS: Seventy-eight undergraduates involved the clinical trial simulation and reported improvement of 1.60 (95% CI, 1.48, 1.80, P < 0.001) in knowledge and 1.82 (95% CI, 1.64, 2.00, P < 0.001) in practice score. 83 undergraduates involved in the observational study simulation and reported improvement of 0.96 (95% CI, 0.79, 1.18, P < 0.001) in knowledge and 1.00 (95% CI, 0.79, 1.21, P < 0.001) in practice. All post-intervention scores were significantly higher than those of the three external control groups, P < 0.001. Higher agreement on the importance of clinical research were correlated with greater improvements in scores. Undergraduates in pre-post study showed high confidence in doing a future clinical research. CONCLUSION: Our study provides evidence supporting the integration of simulation into clinical research curriculum for medical students. The importance of clinical research can be emphasized during training to enhance learning effect.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Development of a core outcome set for cardiac rehabilitation in patients with myocardial infarction: a study protocol.

INTRODUCTION: Heterogeneous outcome reporting is common in clinical trials focused on cardiac rehabilitation for myocardial infarction (MI); this practice often results in the exclusion of data from clinical trials in systematic reviews. Developing a core outcome set (COS) may solve this problem. METHODS AND ANALYSIS: We will first identify a preliminary list of outcomes through a systematic review. Next, we will conduct semistructured interviews with patients to explore additional potential outcomes deemed important by patients. Then, we will engage various stakeholders such as clinicians, researchers and methodologists in two Delphi survey tends to refine and prioritise the identified outcomes. Subsequently, we will gather insights directly from patients with MI by administering plain language patient surveys; patients will be involved in questionnaire development. Finally, we will hold two face-to-face consensus meetings for patients and other stakeholders to develop the final COS for cardiac rehabilitation in MI. ETHICS AND DISSEMINATION: The Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine approved this study (2022DZMEC-349). The final COS will be published in a peer-reviewed journal and disseminated in conferences. TRIAL REGISTRATION: We registered this study in the Core Outcome Measures in Effectiveness Trials Initiative (COMET) platform. REGISTRATION NUMBER: 1725 (http://www.comet-initiative.org/studies/details/1725).

Clinical biomarker-based biological aging and risk of benign prostatic hyperplasia: A large prospective cohort study.

Objective: Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging-related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort. Method: A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera-Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM-BAge and PhenoAge-BAge measures based on the Levine method. The KDM-AAge and PhenoAge-AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk. Results: During a median follow-up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM-AAge (P = 0.041) and PhenoAge-AAge (P = 0.020). Compared to the balance comparison group (-1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000-1.223) for KDM-AAge and 1.180 (95% CI, 1.068-1.303) for PhenoAge-AAge, respectively. For PhenoAge-AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374-2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088-1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge-associated risk of BPH was independent of and additive to genetic risk. Conclusions: Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH.

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).

BACKGROUND: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. METHODS: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. RESULTS: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. CONCLUSIONS: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.