Journal of Clinical and Translational Science最新文献

{"title":"Development and psychometric analyses of a mentee competency self-assessment (MCSA) tool.","authors":"So Hee Hyun, Jonathan M Orsini, Kimberly Spencer, Stephanie C House, Christine Pfund","doi":"10.1017/cts.2026.10727","DOIUrl":"10.1017/cts.2026.10727","url":null,"abstract":"<p><strong>Introduction: </strong>This paper presents the development and psychometric analyses of the Mentee Competency Self-Assessment (MCSA), a tool designed to evaluate mentee skills in research mentoring relationships and assess the <i>Mentoring Up</i> curriculum. By assessing mentee competencies across diverse settings, the study aims to enhance understanding of mentoring dynamics and highlight the importance of considering both mentor and mentee perspectives in assessing mentorship effectiveness.</p><p><strong>Methods: </strong>The 26-item MCSA instrument was developed based on the validated Mentoring Competency Assessment (MCA) to evaluate the <i>Mentoring Up</i> curriculum. Data was obtained from 401 mentees who attended <i>Mentoring Up</i> workshops between 2015 and 2022. Principal component analysis (PCA) was performed with varimax rotation to examine the internal structure of the MCSA. A team of mentoring experts independently reviewed and reached consensus on component alignment. Confirmatory factor analysis (CFA) and internal consistency were performed to assess construct validity and reliability.</p><p><strong>Results: </strong>Factor and reliability analyses support an eight-component structure of a 22-item MCSA. All parameter estimates were significant, and the components demonstrated acceptable to high internal consistency (Cronbach's alpha = 0.58-0.90).</p><p><strong>Conclusions: </strong>The final 22-item scale (MCSA-22) aligns with eight competencies and is now suited for measuring mentee mentorship skills. Given the modest sample size and other study limitations, replication of this proposed modification of the MCSA is an important next step. Additional recommendations for future scale development are offered.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e70"},"PeriodicalIF":2.7,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Return of results is important to heterogeneous research participants: A single-site survey.","authors":"Sheila M O'Byrne, Ismael Castaneda, Rayheann-NaDeja Collins, Dora Ventura, Siddiq Mohamed, Alejandra N Aguirre, Mary Beth Terry, Elizabeth Cohn, Jiying Han, Cheng-Shiun Leu, Shing M Lee, Soumitra Sengupta, Rhonda G Kost, Karen Marder, Paul S Appelbaum, Nancy S Green","doi":"10.1017/cts.2026.10716","DOIUrl":"10.1017/cts.2026.10716","url":null,"abstract":"<p><strong>Background: </strong>Clinical researchers at U.S. academic health centers are becoming more attuned to the perspectives and values of research participants, seeking to partner with them to enhance their satisfaction and improve recruitment strategies.</p><p><strong>Methodology: </strong>We surveyed current or recent participants on their perspectives about the return of study results. Through a multi-site consortium of academic medical centers assessing the experiences of research participants using an online satisfaction survey, we added three questions to our institution's version of the survey to assess the value placed on return of research results (RoR) to current or recent adult participants. Survey participants were offered anonymous participation using four different recruitment mechanisms (\"sites\") hosted by our institution. Most recruitment was disease-agnostic.</p><p><strong>Results: </strong>A total of 506 heterogeneous respondents completed the survey. Although differences were found across recruitment sites, 73% of all participants desired and 49% expected to receive their own RoR, while 61% expected to receive the study's aggregate results. The importance of receiving their own results was especially salient for respondents from historically underserved communities, identifying as non-white, Latino/Hispanic, primarily Spanish-speaking, older or less educated. Respondents' sex was not a significant factor in preferences for return of results.</p><p><strong>Conclusions: </strong>Our results indicated our research participants' expectations and perceived value of receiving the results from studies in which they participated, especially their individual results. This study provides direct evidence of the desires of our research participant community and suggests that institutional support for the return of study results would better serve participants' interests and expectations in future research.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e67"},"PeriodicalIF":2.7,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147771494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative models and synthetic data in clinical prediction models: Promoting consistency, reproducibility, and transparency.","authors":"Anthony A Mangino, Taha Ahmed, Vincent L Sorrell","doi":"10.1017/cts.2026.10729","DOIUrl":"10.1017/cts.2026.10729","url":null,"abstract":"<p><strong>Introduction: </strong>Reproducibility, consistency, and transparency are essential to responsible and ethical scientific inquiry, though practices supporting these qualities are often neglected. However, in many cases data are confidential or otherwise unable to be shared publicly. This tutorial describes a method utilizing generative adversarial networks (GANs) to create synthetic data that are sufficiently similar to the original dataset in such cases where the source data cannot be shared or where the source data are too sparse as to internally validate results.</p><p><strong>Methods: </strong>Utilizing an exemplar study that aimed to create a clinical prediction model employing a novel echocardiographic measurement to differentiate between acute coronary syndrome and Takotsubo syndrome, we demonstrate the procedure of fitting a GAN and evaluating the resulting synthetic dataset against the results from the source dataset using conventional analytic methodologies. Further, we include relevant R code and output from this process to aid in implementation.</p><p><strong>Results: </strong>The procedure we detail yielded a synthetic dataset that was largely similar to the source data used in univariate descriptive statistics, significance testing comparing variables across datasets, data visualizations, and yielded largely comparable secondary model fit and accuracy metrics.</p><p><strong>Conclusions: </strong>We demonstrated that through the implementation of a well-tuned GAN, synthetic data can be generated as a sufficiently faithful simulacrum of the source data for the purposes of internal validation, transparency of method, and reproducibility of analytic results.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e65"},"PeriodicalIF":2.7,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13122389/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital and community-engaged approaches to improve research participant recruitment - Advice from forty convened experts.","authors":"Loretta M Byrne, Hailey N Miller, Cyd Lacanienta, Cassie Lewis-Land, Daniel E Ford, Cheryl R Dennison Himmelfarb","doi":"10.1017/cts.2026.10720","DOIUrl":"10.1017/cts.2026.10720","url":null,"abstract":"<p><p>Achieving enrollment goals is essential for the successful completion of a clinical trial. This includes enrolling a sample size that provides adequate power and engaging a study population that supports generalizability of research findings. Yet, trial participation is routinely hindered by its complexity, associated risks, and frequently cited barriers to participation including lack of awareness, low trust/mistrust, and logistical burdens that make participation of low value or unrealistic to potential participants [1,2].</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e62"},"PeriodicalIF":2.7,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The FOCUS model: A guide to promote patient partnership in clinical trial designs.","authors":"Jennifer Catena Davis, Patrick Corr, Amy Raymond, Sabrina Figueiredo","doi":"10.1017/cts.2026.10719","DOIUrl":"10.1017/cts.2026.10719","url":null,"abstract":"<p><strong>Background: </strong>Evidence suggests that partnerships between researchers and patient communities result in clinical trials that better reflect the patient experience, but there are few documented and successful models of such partnerships. Within the clinical research landscape, the Duchenne Muscular Dystrophy (DMD) community has emerged as a positive outlier in this regard, having engaged in a research partnership over multiple years and in the approval of several disease-modifying therapies where there were previously none.</p><p><strong>Methodology: </strong>The successful partnership factors observed in the DMD community were mapped onto the three levels of the Behavior Change Wheel framework. This mapping created an academic model of patient community readiness for research partnerships. The academic model was further translated into a user-friendly, pragmatic model and blueprint.</p><p><strong>Results: </strong>The FOCUS Model outlines the development of a research partnership from the ground up, where each theme represents an essential component of the partnership structure. Accompanying this model is a discussion guide that communities can use in meetings or workshops to assess and enhance their readiness for research partnerships.</p><p><strong>Conclusions: </strong>The FOCUS model and its blueprint offer generalizable approaches that other rare disease patient communities can leverage to foster effective and lasting partnerships with scientific, medical, and regulatory stakeholders. The approach proposed in this study has the potential to help both the clinical research community and rare disease patient communities overcome typical barriers to rare disease clinical development.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e77"},"PeriodicalIF":2.7,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building engagement in healthy aging outcomes research with older women with lived experience of incarceration.","authors":"Sherri Anderson, Leigh Anne Covington, Shamika Davis, Amanda Emerson, Déon Cox Hayley, Catherine Johnson, Jill Peltzer, Bernard Schuster, Janet Severine, Catherine Siengsukon, Sharla Smith, Mary Taylor, Amanda Thimmesch, Alexandria Thompson","doi":"10.1017/cts.2026.10724","DOIUrl":"10.1017/cts.2026.10724","url":null,"abstract":"<p><p>Community-dwelling older women (age >50) with <i>lived experience of incarceration</i> (LEI) are an increasing yet mostly unacknowledged group with long-term health challenges. Research addressing aging-related health in older women with LEI is rare, and almost none engages collaboratively and equitably with older women to understand their aging-related health needs, preferences, and priorities. To foster awareness and readiness for engagement in outcomes research, we co-planned, implemented, and evaluated a sequence of interlinked activities and outputs with older women, clinicians and researchers, and community advocates. The <i>Older Women Leading Healthy Aging Research Together</i> (OWLHART) network met monthly from November 2024 to June 2025. Four to six women, two clinician-researchers, two community advocates, and a project team <i>shared</i> aging-related health beliefs, concerns, and outcome priorities; <i>learned</i> the basics of research and benefits of patient-centered outcomes research; created tools to <i>teach</i> about older women's health after incarceration and the role of research; and developed <i>outreach</i> to disseminate the work. OWLHART models an equity-focused capacity-building approach for research engagement outside the clinical setting and with a patient group that has been overlooked as patients and as research partners.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e73"},"PeriodicalIF":2.7,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the gap: Community insights on effectively sharing research findings with the public.","authors":"Jennifer Mary Poger, Jessica Abrams Schrodel, Paula C Moodie, Candace Bordner, Nehath Sheriff, Jennifer B McCormick, Jennifer L Kraschnewski","doi":"10.1017/cts.2026.10717","DOIUrl":"10.1017/cts.2026.10717","url":null,"abstract":"<p><strong>Introduction: </strong>Funders increasingly emphasize the ethical imperative to return research results, yet researchers often lack training and clear strategies for effectively sharing findings with lay audiences. While publishing in academic journals is standard practice for dissemination, little guidance exists on translating findings for communities, particularly in rural areas. This qualitative community-guided pilot project aimed to explore and strengthen strategies for sharing study results in accessible ways.</p><p><strong>Methods: </strong>The Penn State Clinical and Translational Science Institute conducted six semi-structured focus groups in Fall 2023 with geographically dispersed Pennsylvanians. Focus groups introduced participants with and without prior research experience to evidence-based and novel dissemination methods - such as lay summaries and data walks - to gather feedback on preferences and experiences. Data were coded and analyzed using MAXQDA, achieving strong interrater reliability (kappa > 0.70). Themes were developed inductively.</p><p><strong>Results: </strong>Focus group participants (<i>N</i> = 45) were predominantly women (<i>N</i> = 39, mean age = 56); 10% identified as Black/African American. Geographically, 49% were rural, 44% suburban, and 7% urban. Major themes included lack of effective communication in the research process, poor representation, and limited access to results. Most participants had never received study findings. Participants preferred receiving easy-to-understand summaries shared by individuals with established community relationships. They also found data walks, where researchers bring key findings printed on posters to community events, to be especially engaging and valuable.</p><p><strong>Conclusion: </strong>Community-informed dissemination approaches can increase research transparency, engagement, and results translation in communities, particularly in rural areas where accessibility is limited.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e69"},"PeriodicalIF":2.7,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perceptions of health research participation in rural and urban Pennsylvanians.","authors":"Jennifer B McCormick, Jennifer Mary Poger, Sandesh Bhandari, Courtney Whetzel, Angel Collie, Alyson G Eggleston, Jennifer L Kraschnewski, Orfeu M Buxton","doi":"10.1017/cts.2026.10718","DOIUrl":"10.1017/cts.2026.10718","url":null,"abstract":"<p><strong>Introduction: </strong>Emerging evidence describes the experiences of individuals participating in health research, but insights into the barriers and motivations around research participation in rural communities are limited. We developed and administered a human-centered, evidence-informed survey to assess motivators and barriers to research participation among adults in Pennsylvania.</p><p><strong>Methods: </strong>The online survey captured differences between individuals with and without prior research participation and living in rural and urban settings. We hypothesized that individuals with prior research experience would report different motivators and barriers than those who had never participated in research. We also anticipated that rural and urban respondents would differ in their reported motivators and barriers to participation.</p><p><strong>Results: </strong>Participants (n = 284, 75% female, 63% urban, 73% with prior research) completed the survey in spring of 2025. Overall top motivators to research participation included a willingness to \"contribute to knowledge and medicine,\" to \"help others,\" to \"make a difference,\" \"because the research was personally important,\" and \"financial compensation.\" Top barriers included an \"inconvenient research site,\" \"limited transportation access,\" and \"time/work constraints.\" A variety of motivators and barriers differed by prior research experience. There were no significant differences between the proportion of rural and urban prior research participants who endorsed any of the motivators or barriers. Rural, non-research participants drew greater motivation from \"family influence\" and \"volunteering commitment.\"</p><p><strong>Conclusion: </strong>The results of this work can inform the development of targeted strategies to improve research engagement, particularly among rural populations.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e72"},"PeriodicalIF":2.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable machine learning for predicting coronary heart disease risk in patients with carotid atherosclerosis: A retrospective study with SHAP and decision curve analysis.","authors":"Lei Zhang, Mengke Lyu, Mingyuan Du, Yizhuo Li, Haifeng Yan, Xiaohui Li, Wenshuang Niu, Lizhi Pang","doi":"10.1017/cts.2026.10722","DOIUrl":"10.1017/cts.2026.10722","url":null,"abstract":"<p><strong>Background: </strong>Carotid atherosclerosis is associated with increased coronary heart disease (CHD) risk, yet current risk models lack specificity and interpretability for this population. This study aimed to develop explainable machine learning (ML) models to predict CHD in these patients.</p><p><strong>Methods: </strong>We retrospectively analyzed 487 patients with carotid atherosclerosis (191 CHD, 296 non-CHD) from January 2022 to July 2025. Thirty-eight variables were collected, including demographic, clinical, and biochemical indicators. LASSO regression identified six key predictors. Seven ML models were trained and evaluated using area under receiver operating characteristic curve (AUC), PRC-AUC, calibration curves, and decision curve analysis (DCA). SHAP was applied to interpret the best-performing model.</p><p><strong>Results: </strong>Logistic regression model achieved the highest test-set performance (AUC = 0.827; PRC-AUC = 0.752), with strong generalizability and calibration. SHAP analysis identified age and diastolic blood pressure as the most influential features, aligning with model coefficients. DCA demonstrated superior clinical net benefit of the logistic regression model across probability thresholds.</p><p><strong>Conclusion: </strong>A six-variable logistic model provides accurate and interpretable CHD risk prediction in patients with carotid atherosclerosis. Its transparency and clinical utility support its integration into personalized risk management.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e57"},"PeriodicalIF":2.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13058763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147645634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Characteristics of participants in the national research mentoring network studies - CORRIGENDUM.","authors":"So Hee Hyun, Emma Dums, Fátima Sancheznieto, Kimberly Spencer, Julie M Hau, Jenna Griebel Rogers, Christine Pfund","doi":"10.1017/cts.2026.10714","DOIUrl":"10.1017/cts.2026.10714","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1017/cts.2025.10133.].</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e41"},"PeriodicalIF":2.7,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12975615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}