Journal of Clinical and Translational Science最新文献

{"title":"Flexible outlier detection in multicenter clinical trials.","authors":"Joseph Rigdon, Santiago Saldana, Sawyer Welden, W Jack Rejeski, Edward Melanson, Neil Johannsen, Cynthia Stowe, Michael Miller","doi":"10.1017/cts.2026.10765","DOIUrl":"10.1017/cts.2026.10765","url":null,"abstract":"<p><strong>Introduction: </strong>Multicenter clinical trials have become increasingly larger and more complex yet assuring high-quality data remains an essential task for data coordinating centers.</p><p><strong>Methods: </strong>A flexible algorithm is presented to exhaustively search for potential data anomalies by participant and site. The algorithm proceeds as a three-phase collaboration between the data coordinating center and clinical sites. First, participant-level data are examined in a univariate approach for all relevant variables. Values at the extreme tails of the distribution that also lie outside range checks and are previously unverified by clinical sites are deemed potential outliers. Second, participant-level data are examined in a multivariate machine learning approach among meaningful groups of related variables, <i>e.g.,</i> weight and body mass index. Third, site-level differences are characterized using statistical tests and standardized differences, both adjusted and unadjusted for site demographics. Findings are discussed with sites and, if needed, alterations can be made to procedures for data collection. For illustration, the algorithm is applied to data collected in the Molecular Transducers of Physical Activity Consortium (MoTrPAC) study.</p><p><strong>Results: </strong>Application of the algorithm to MoTrPAC yielded an evaluation of over 1.9 million observations in <i>n</i> = 1029 study participants. Numerous individual univariate, multivariate, and site-level outliers were identified that were previously unidentified by existing data evaluation processes.</p><p><strong>Conclusion: </strong>It is recommended to apply this algorithm to a subset of participants early in a study, with repeated explorations over subsequent intervals throughout the study. The goal is to maximize data quality, particularly critical to the increasing occurrence of open-source, data resources.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e104"},"PeriodicalIF":2.7,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13323618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148375884","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":"Embracing complexity: An opportunity for developmental evaluation.","authors":"Brian J Biroscak, Peter S Hovmand","doi":"10.1017/cts.2026.10767","DOIUrl":"10.1017/cts.2026.10767","url":null,"abstract":"","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e102"},"PeriodicalIF":2.7,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13312358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148352173","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":"Comparing the effectiveness of master's-level clinicians and peer supports for the delivery of attachment and biobehavioral catchup.","authors":"Elisabeth Conradt, Claire Dahl, Kristen Cotton, Erin Hoffman, Evette Horton, Mary Dozier, Danielle S Roubinov","doi":"10.1017/cts.2026.10764","DOIUrl":"10.1017/cts.2026.10764","url":null,"abstract":"<p><p>The goal of this study was a re-analysis of a randomized controlled trial evaluating the effectiveness of Attachment and Biobehavioral Catchup (ABC), an early childhood parenting intervention. We compared the effectiveness of ABC for enhancing parental sensitivity and child behavior among 79 dyads who received the intervention as delivered by peer support specialists (n = 49 dyads) or master's-level clinicians (n = 30 dyads). Peer support parent coaches were non-inferior at enhancing parental sensitivity and parental intrusiveness as parent coaches with a master's degree. This research has important clinical implications for increasing access to ABC for parents involved in the child welfare system.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e108"},"PeriodicalIF":2.7,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470956","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 capacity in translational research ethics among early-stage investigators: A pilot course on the ethics of the clinician-researcher dual-role.","authors":"Laura Stamm, Maya Daniello, Ahona Shirin, Patricia Luck, Mical Raz, Nicholas R Mercado, Scott McIntosh, Lainie Friedman Ross, Jonathan Herington","doi":"10.1017/cts.2026.10770","DOIUrl":"10.1017/cts.2026.10770","url":null,"abstract":"<p><strong>Purpose: </strong>Translational researchers often occupy dual roles as clinicians and investigators, creating ethical tensions that are inadequately addressed by traditional training in research ethics or clinical ethics. This study aimed to design, pilot, and evaluate a short course to improve early-stage translational researchers' self-efficacy with respect to navigating conflicts between their roles as researchers and clinicians or community advocates.</p><p><strong>Methods: </strong>An inter-disciplinary team at University of Rochester developed an 8-module course aimed at improving self-efficacy in moral decision-making. Using Rest's Four Component Model, the eight course sessions focused on moral sensitivity, reasoning, motivation, and action with respect to dual-role ethical issues such as expanded trial access, ancillary obligations, and return of results. Thirteen early-stage translational researchers participated in a pilot course delivered in person from March through May 2024. Evaluation included pre-and post-course surveys and focus groups.</p><p><strong>Results: </strong>Participants demonstrated significant improvements in self-efficacy for moral sensitivity (mean increase .79, p < .001), moral reasoning (mean increase .80, p = .001), and ability to take moral action (mean increase .67, p = .002). Participants' attitudes toward translational research were generally stable. Focus groups revealed that participants highly valued the course, but desired greater curricular cohesion and more guidance on implementation strategies.</p><p><strong>Conclusions: </strong>This pilot course successfully enhanced early-career translational researchers' self-efficacy for ethical decision-making in dual-role contexts. The findings suggest that ethics education tailored to translational researchers' unique challenges can meaningfully improve their capacity to navigate complex ethical tensions.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e111"},"PeriodicalIF":2.7,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669666","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":"Individual patient data meta-analysis and systematic review evaluating camostat mesilate to treat COVID-19.","authors":"Haley Hedlin, Els Tobback, Justin Lee, Yiwen Wang, Ilaria Dragoni, Daniel C Anthony, Kevin Dhaliwal, John Norrie, Sarah Halford, Jose Gotes, Mariana Moctezuma, Antonio Olivas-Martinez, Chaitan Khosla, Upinder Singh, Jesper Damsgaard Gunst, Alonso Valdez, David Kershenobich, David Boutboul, Ole S Søgaard, Marie-Angélique De Scheerder, Manisha Desai, Julie Parsonnet","doi":"10.1017/cts.2026.10769","DOIUrl":"10.1017/cts.2026.10769","url":null,"abstract":"<p><strong>Background: </strong>In the COVID-19 pandemic, several phase II and III randomized trials were launched to evaluate the effectiveness of camostat, an orally administered TMPRSS2 inhibitor previously approved for other indications, for treating SARS-CoV-2 infections. Owing to the rapidly changing landscape during the pandemic, many of these trials were unable to reach completion. Further, methods for synthesizing trials that were launched and not completed were critical.</p><p><strong>Methods: </strong>This systematic review aimed to consolidate global evidence by identifying placebo controlled, randomized trials of camostat and analyzing their collective clinical and virologic impact on SARS-CoV-2 through an individual patient data meta-analysis (IPDMA). We harmonized data from the studies and utilized Bayesian statistical models to assess virologic outcomes (measured by the rate of change in viral shedding) and clinical outcomes (based on the time to the first of two consecutive symptom-free days), adjusting for age and sex.</p><p><strong>Results: </strong>The IPDMA incorporated data from six countries, totaling 431 patients across the studies; 118 patients contributed data for the primary virologic outcome and 240 for the clinical symptom outcome. Camostat did not improve the rate of change in viral load (difference in rate of change = 0.11 Ct value/day higher, 95% credible interval 2.04 lower to 2.23 higher) or time to symptom resolution (hazard ratio = 0.87, 95% credible interval 0.51, 1.55) when compared to placebo.</p><p><strong>Conclusions: </strong>Despite its theoretically promising mode of action, camostat did not demonstrate a statistically significant virologic or clinical benefit in treating COVID-19, highlighting the complexity of drug repurposing in emergency health situations.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e110"},"PeriodicalIF":2.7,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148470967","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":"Navigating the transition from early-stage investigator to mid-career: Challenges and opportunities for developing careers in diabetes translational research.","authors":"Tainayah W Thomas, Stephanie A Hooker, Chibuzor Abasilim, Alyce S Adams, Foster Osei Baah, Sarah S Farabi, Julie Schmittdiel, Sarah A Stotz, Luis A Rodriguez","doi":"10.1017/cts.2026.10757","DOIUrl":"10.1017/cts.2026.10757","url":null,"abstract":"<p><p>Diabetes mellitus is a significant health burden, and recent reports indicate an increase in diabetes-related complications in the United States (U.S.). The mission of the National Institute of Diabetes and Digestive and Kidney Diseases Centers for Diabetes Translational Research (CDTR) is to improve the translation of research findings in diabetes prevention and treatment by supporting research across the translational science spectrum. To facilitate this mission, it is imperative to support multidisciplinary translational early-stage investigators (ESIs) to address the societal-, environmental-, and individual-level factors that impact diabetes outcomes. In this special communication article, we characterize the challenges, opportunities, and needs of ESIs transitioning to mid-careers whose goals are to develop a career in diabetes translational research. These challenges and opportunities were collected from 34 ESIs and 19 senior faculty members from 23 institutions across the U.S. who attended the inaugural CDTR National Enrichment Program Conference. Our findings aim to promote the engagement of ESIs in diabetes translational research and provide guidance in strengthening and sustaining their research careers.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e119"},"PeriodicalIF":2.7,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669631","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":"A machine learning approach to identify at-risk populations for yellow fever vaccination.","authors":"Madison Farnsworth, Kostiantyn Botnar, Justin T Nguyen, Riley K Watson, Trevor L Murphy, Susan L F McLellan, Kamil Khanipov, George Golovko","doi":"10.1017/cts.2026.10754","DOIUrl":"10.1017/cts.2026.10754","url":null,"abstract":"<p><strong>Introduction: </strong>Vaccinations play a crucial role in public and personal health. Vaccines such as the Yellow Fever 17D vaccine are effective at preventing disease and provide lifelong immunity with few adverse events. Leveraging the increase in electronic medical records and the widespread use of machine learning algorithms in healthcare, this study aims to investigate and develop an algorithmic framework for analyzing clinical data on Yellow Fever 17D vaccinations in order to predict at-risk populations for adverse events following immunizations. This research incorporates parameters from the patient's medical history and demographic information.</p><p><strong>Methods: </strong>To build our analytic framework, we tested five machine learning algorithms: random forest, gradient boost, logistic regression, XGBoost, and Bernoulli naïve Bayes. We cleaned and managed the datasets through EHRchitect software. We assessed the performance of the algorithms using standard metrics, including precision, recall, F1-scores, accuracy, and the area under the receiver operating characteristic curve. Additionally, we implemented population sampling to mitigate potential biases common in clinical data and performed parameter ranking analysis to identify the most influential features in classifying patient reaction outcomes.</p><p><strong>Results: </strong>We found that logistic regression produced the highest performance, and through hyperparameter optimization, we developed a framework with precision, accuracy, and ROC scores of 87.7%, 99.0%, and 0.818, respectively.</p><p><strong>Conclusion: </strong>In conclusion, this study examined five modeling algorithms to establish a framework for analyzing real-world data and to predict at-risk populations for adverse events following immunization. We incorporated a logistic regression algorithm to assess our clinical data to achieve a high-precision performance model for classifying patient predictions.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e106"},"PeriodicalIF":2.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471001","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":"PREP-aring is worth it: Success of the Case Western Reserve University post-baccalaureate research education program and its scholars.","authors":"Dana C Crawford, Esteban Vazquez-Hidalgo, Hua Lou","doi":"10.1017/cts.2026.10758","DOIUrl":"10.1017/cts.2026.10758","url":null,"abstract":"<p><p>The National Institute of General Medical Sciences (NIGMS) Post-baccalaureate Research Education Program (PREP) was a research-intense, one-year training program for recent college graduates from experiential backgrounds uncommon in science who intended to matriculate with a PhD program in preparation for a career in biomedical research. Case Western Reserve University had an NIGMS-supported PREP (CasePREP) from 2007 until 2025, the year NIGMS terminated the program and early expired the PREP funding mechanism. We report here the extent that CasePREP, with its 108 Scholars, met program goals as well as the economic and scientific impact NIGMS-supported PREP has had for a variety of stakeholders. CasePREP was a great success as measured by its major outcomes: matriculation into a PhD or MD/PhD program (83%) and completion of degree (51 Scholars to date). CasePREP Scholars have persisted in science and have made substantial contributions to the scientific workforce and enterprise. PREPs in general provided a crucial bridge for research talents who had little prior research opportunities to realize their potential and career goals. NIGMS termination of PREP support has negative economic consequences and endangers an important pathway to a career in science for Americans who would not otherwise have similar opportunities.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e113"},"PeriodicalIF":2.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669720","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":"Research on the road: Partnering with community emergency medical services to expand access to clinical trials.","authors":"Laurel O'Connor, Alex Ulintz, Joel Rowe, Peter K Lindenauer, Apurv Soni","doi":"10.1017/cts.2026.10761","DOIUrl":"10.1017/cts.2026.10761","url":null,"abstract":"<p><p>Traditional site-based clinical trials impose substantial logistical burdens that limit participation among individuals with geographic, mobility, transportation, and socioeconomic constraints, thereby undermining research equity and generalizability. Although pragmatic and decentralized trial designs seek to address these barriers, many health systems lack a mobile clinical workforce capable of conducting in-person research activities in community settings. Mobile Integrated Health (MIH) and Community Paramedicine (CP) programs, which leverage community paramedics and EMTs (MIH-CPs) to provide field-based care, may provide scalable infrastructure to support decentralized research delivery. We describe an institutional model that cross-trains and integrates MIH-CPs into research teams. Using study protocols, training materials, operational procedures, and implementation documents from five studies, we analyzed staffing models, credentialing and training processes, workflow integration, fidelity monitoring, and ethical and safety considerations. The included studies spanned pragmatic randomized and hybrid trials, and observational cohort studies across diverse clinical populations. MIH-CPs conducted recruitment, consent, assessments, intervention delivery, and data collection. Structured training, defined communication pathways, and integration with established clinical workflows supported protocol fidelity, participant safety, and operational feasibility. Embedding mobile community-based clinicians within research teams represents a pragmatic, equity-oriented strategy to expand decentralized trial capacity, reduce participant burden, and accelerate translation of evidence into real-world practice.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e96"},"PeriodicalIF":2.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13323617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148375893","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":"Mapping the landscape of dissemination and implementation science across the CTSA consortium: A multi-domain environmental website scan.","authors":"Jing Li, Naomi Duffort, Salisa Westrick, Renee Heffron, Abigail Gamble, Bertha Hidalgo, Katherine Mills, Kirsten Dorans, Kristine Ria Hearld, Larry R Hearld, Lisa Zubkoff, Peter T Katzmarzyk, Michael Mugavero","doi":"10.1017/cts.2026.10760","DOIUrl":"10.1017/cts.2026.10760","url":null,"abstract":"<p><p>Despite substantial investment in clinical and translational research, only a small proportion of evidence-based interventions are adopted and sustained in routine practice, contributing to persistent delays between discovery and population benefit. Dissemination and implementation (D&I) science is a critical discipline for addressing this gap, and the NIH Clinical and Translational Science Award (CTSA) program (established 2006) has been strategically positioned as a national infrastructure to advance D&I capacity. We conducted a national environmental scan of publicly available websites and documents from all 66 CTSA hubs (May-July 2025), using a structured extraction tool to capture D&I-specific activities across seven domains: institution and community partnerships, formal D&I organizational structures within the CTSA, consultation services, collaborative programming, training opportunities, educational offerings, and pilot funding mechanisms. Findings reveal substantial heterogeneity in D&I science activities across CTSA hubs; 45% had a formal D&I unit, 54% offered D&I consultation services, and 37% provided collaborative programming. Structured workforce development was limited: 12% offered D&I-focused training grants, 15% offered structured educational programs, and 15% provided D&I-specific pilot funding. Consultation models varied widely in scope, access, and evaluation practices. These findings demonstrate uneven development of D&I science infrastructure across CTSAs and highlight opportunities to strengthen capacity nationally.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":"10 1","pages":"e97"},"PeriodicalIF":2.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13312347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148352132","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}