Objectives/Goals: The study’s objective was to use real-world data to compare machine learning approaches for predicting colorectal cancer (CRC) screening status: (1) a Random Forest model optimized for overall accuracy and (2) Feasible Solution Algorithm (FSA) models focused on interpretability and identifying subgroups at risk of being unscreened. Methods/Study Population: The data included 22,549 adults aged 45–75 who visited a UK King’s Daughters Medical Center (UKKD) primary care site between 12/1/2023–11/30/2024, across five Appalachian counties. Patients with total colectomy, colon cancer, advanced illness, hospice care, dementia, and/or frailty were excluded. After complete-case analysis, 18,533 records remained and were split 80/20 for training/testing. Two machine learning methods, random forests and FSA, were applied to identify key predictors of CRC screening status. Within the FSA framework, logistic regression models were fitted using the training data, and model performance metrics were subsequently evaluated using the testing dataset. Results/Anticipated Results: Overall (n=22,549), 65.6% of patients completed CRC screening. The mean age was 59 years (SD = 8.2); most were White (97%), 40% had Medicare insurance, and 68% had multiple non-primary care provider (PCP) visits. The Random Forest model demonstrated the highest overall accuracy of 69% and had notably high sensitivity, correctly identifying approximately 9 of 10 screened patients. On the other hand, the FSA model offered greater specificity (65%) and interpretability, with the 2-predictor model identifying smokers and those with 0 non-PCP visits as the vulnerable subgroups who are least likely to be screened. Discussion/Significance of Impact: FSA identified smoking status, insurance type, number of non-PCP clinic visits, and age as key factors relating to CRC screening behavior. These findings can help clinicians and public health teams identify unscreened patients, guide targeted outreach, and advance translational strategies to improve population health.

Objectives/Goals: Interdisciplinary teams are central to modern science, but measuring their success remains difficult. Traditional metrics like publications or citations often miss key contributions, such as community engagement, mentorship, and lasting collaboration. New approaches are needed to capture these goals. Methods/Study Population: We introduce a framework aligning success metrics with a team’s organizing priorities, illustrated through two contrasting models: Team A, a time-bound, community-focused group emphasizing rapid deliverables and public health partnerships, and Team B, a mentorship-driven, development-focused group prioritizing capacity building and long-term growth. These teams share a commitment to advancing translational research but differ in how they pursue these goals. Team A emphasized centralized coordination, shared ownership of goals, yielding timely scholarly outputs, and setting the stage for future collaborations. Team B prioritizes long-term development, sustained collaboration, and the mentorship of early-career faculty, even if traditional academic productivity metrics accrue more gradually. Results/Anticipated Results: These contrasting approaches raise an important question: how should success be defined and measured for teams with very different goals, structures, and outputs? From these cases, we derive four Collaborative Guideposts: Time Perspective, Unit of Assessment, Desired Outcomes, and Sustainability, which provide a systematic way to identify and select metrics that meaningfully capture a team’s contributions and impact. By offering a structured yet adaptable framework, these guideposts enable teams to track progress, monitor outcomes, and ensure alignment with their unique goals, operating environments, and stages of development. This approach contributes to advancing validated models of team science by providing a practical method for tailoring evaluation criteria to diverse collaborative contexts. Discussion/Significance of Impact: By grounding evaluation in team-specific goals, this framework supports more accurate assessments of collaborative impact. Guideposts provide a flexible structure to align metrics with context, guide discussions of outcomes, and evaluate teams on productivity, collaboration, mentorship, and long-term scientific progress.

Objectives/Goals: Given the close proximity of two academic institutions with CTSAs, we piloted distinct training topics – psychological safety and evaluation of opportunities – with K and T scholars at both academic hubs. Each topic was originally developed and delivered by one of the partnering hubs, allowing for cross-institutional sharing of expertise. Methods/Study Population: The team science educators at both institutions had a series of conference calls to share information about the types of professional development activities offered to early-career investigators at respective institutions. Each group then agreed to offer a new workshop at the partner institution. Team 1 presented “Should I Join This Research Team? Strategically Evaluating a New Teaming Opportunity,” while Team 2 presented “Promoting a Positive Culture in Teams.” Our instructional goal was to deliver skills-based training that addressed opportunities and challenges unique to research teams. Each session incorporated interactive methods, including scenario analysis and discussion, think-pair-share activities, and short video clips. Results/Anticipated Results: Likert-scale items and open responses will be completed by workshop participants before and after participating in the workshops. Attendees will evaluate their confidence in their perceived abilities related to workshop objectives and their perceptions of the utility and application of the content. In addition to the trainee evaluations, the two institutional teams completed a joint qualitative interview to provide feedback, discuss andragogical strategies to improve educational delivery and impact, and assess how to leverage the expertise of each team to provide future team science programming across the two institutions. Given that the workshops are scheduled to be completed by November 2025, the results will be collated and shared at the Translational Science 2026 conference. Discussion/Significance of Impact: We believe partnerships can work in a variety of settings and with multiple topics to enrich the experiences of both early-career investigators and team science educators at partnering hubs. The collaboration also promotes sharing of expertise, content, and resources across institutions which expands educational content across CTSAs.

Objectives/Goals: Collaborative agreements are vital for building strong partnerships, particularly during the formation of new research teams. To support team scientists during critical stages of team formation, we developed a flexible, on-demand eLearning module on collaborative agreements with examples and guidelines for developing tailored agreements. Methods/Study Population: The on-demand Collaborative Agreement module empowers learners to complete a full overview of collaborative agreement concepts or to directly access topics of interest within the module. Designed for real-time applications, it includes conversation prompts, downloadable templates, and example scenarios to guide team building and maintenance. To date, the module has been used by clinical and translational science trainees, early-career investigators, faculty participating in mentor training programs, and PIs of a multi-site research project. Early feedback has prompted the development of a consultative service to support teams in developing their own collaborative agreements after team members complete the module. Results/Anticipated Results: We are collecting data through pre- and post-training assessments embedded within the module. Post-assessment also invites respondents to share contact information for follow-up evaluation of their adoption. As of October 2025, we have collected 21 pre-assessment responses and 14 post-assessment responses. We anticipate gathering more data prior to Translational Science 2026. We will summarize assessment data focused on the tool’s effectiveness, learner’s intention to adopt collaborative agreements, and their confidence in building their own agreements. Additionally, we will provide feedback on the value of support services post-training for team implementation of collaborative agreements. Discussion/Significance of Impact: The just-in-time online tool facilitates the use of collaborative agreements for individual learners, teams, and between mentors and mentees. It supports effective collaboration by clarifying shared expectations. Feedback for the module will inform enhancements as well as future translational science workforce development eLearning projects.

Objectives/Goals: The objectives of this project aim to (1) feature strengthened areas of integration between Biostatistics, Epidemiology & Research Design (BERD) and Biomedical Informatics (BMI) Cores, (2) exemplify an inter-core approach to maximize CTSA data service resources, and (3) discuss opportunities to adapt this approach in other CTSA hubs. Methods/Study Population: BERD and BMI Cores are partnered disciplines integral to the mission of CTSA hubs but often operate independently of each other which can result in unfulfilled data resource linkages. In 2023, the University of Kentucky Center for Clinical and Translational Science integrated joint operations between the two cores to maximize research data support services. This project defines three areas of inter-core collaboration between BERD and BMI in CTSA hubs in academic medical centers: (1) collaborative needs assessment for research data support services, (2) joint consultations for study design, analysis, and data extraction, and (3) partnered joint data resource navigation. Process comparisons of previous and current workflows are presented to show the advantages of utilizing collaborative core efforts. Results/Anticipated Results: When operations are integrated, BERD and BMI Cores leverage CTSA resources to a higher capacity, with data-intensive research support becoming more effective and expedited for project timelines and workflows across the CTSA, especially in support of translational science and research. Early evaluation results support improved operation performance metrics and inter-core teamwork satisfaction. With this partnership, BERD and BMI cores strengthen CTSA data research support services including functional datasets, higher-level service, and leveraging institutional resources. Common service issues including cycles of data re-extractions and limited data support are now avoided when BERD and BMI cores address investigator needs with a joint approach. Discussion/Significance of Impact: CTSAs aiming to improve research navigation efficiency might approach joint BERD and BMI core collaborations and adapt these strategies to fit institutional infrastructure. This collaboration can serve as a case study in translational science innovation, addressing persistent CTSA workflow, hiring, and resource allocation.

Objectives/Goals: Our goal is to enhance communication and documentation in collaborative biostatistics by refining data management and metadata processes. We aim to capture critical data collection and generation information, improve transparency and reproducibility, and foster stronger researcher partnerships for more effective collaborations. Methods/Study Population: Traditional statistical analysis plans (SAP) often miss essential contextual knowledge from collaborators, leading to gaps that hinder reproducibility and limit future data use. Biostatistics teams at the University of Kentucky have updated their strategies to better capture important details about data origins and collection processes. By focusing on clear, comprehensive documentation early in the research process, we aim to preserve foundational data insights and improve collaboration efficiency. Our Biostatistics, Epidemiology, and Research Design (BERD) team has established best practices for addressing data management structures with collaborators across medical and healthcare fields – covering all project stages, from initial data collection to metadata creation and dataset finalization. Results/Anticipated Results: We will detail the processes used to improve data management structures and the observed results of these processes. For example, initiating deeper discussions about data origins and collection processes as early as possible in the collaboration has resulted in a more comprehensive project narrative that lays the foundation for effective collaboration. By engaging with project leaders early in the process, we can confirm that critical details about how data were collected and processed are documented, improving both the transparency and reproducibility of research findings. Streamlining the processes of capturing this information makes it more accessible and useful for those with limited statistical backgrounds, which is particularly relevant for faculty and staff in BERD communities and Clinical and Translational Science Awards Programs. Discussion/Significance of Impact: Nuanced data documentation structures are crucial for transforming raw data into meaningful, reusable datasets. Our initiatives promote clear communication, enhanced efficiency, and streamlined workflows. Translational science researchers can benefit from improving data management and metadata to boost long-term collaborative success.

Objectives/Goals: To design a flexible, comprehensive framework for Data Science Units to cultivate sustainable, long-term relationships with Clinical and Translational Science Research Units. Best practices for managing Data Science collaborations are presented to improve the quality and efficiency of research conducted throughout academic health centers. Methods/Study Population: Leaders of Data Science Units across six institutions formed a workgroup to develop guidance and best practices for Data Science Units to establish long-term, sustainable collaborations with Clinical and Translational Science Research Units. This guidance is based on tools and protocols developed and employed by the participating units, which range from larger groups with over 20 partnerships to a unit with three partnerships that is actively working to expand. Importantly, partnerships are highly variable, with some partnerships at one institution representing engagement with over 500 faculty, whereas some partnerships at another institution involve the lab of a single investigator. Results/Anticipated Results: We offer guidance in three domains: (1) Identifying the needs for a new partnership, including assessing required effort and data science expertise, setting partnership priorities, developing formal agreements, and identifying goals and metrics; (2) managing data science teams by implementing regular meetings, creating project intake and prioritization processes, and effort monitoring; and (3) evaluating the successes and failures/gaps of the collaboration by measuring the metrics mapped to the goals. For each domain, we provide specific suggestions on which parties should be involved and how frequently the processes should occur. This guidance is applicable both to larger collaborative partnerships and to smaller, single faculty or staff partnerships, whether they are new or well-established. Discussion/Significance of Impact: Effective collaboration between data scientists and clinical and translational investigators is key to advancing data-driven research. The guidance and resources are presented to support Data Science Units in successfully managing long-term collaborations through goal-development, evaluation, and adapting to evolving research needs.

A clinical and translational scientist (CTS) often seeks to increase their knowledge of statistical topics to effectively conduct biomedical research studies. A common method for obtaining this knowledge is through existing online educational materials that are suggested by a biostatistical collaborator or identified by the CTS. However, the volume of available educational materials on diverse statistical topics makes the task of identifying high-quality educational resources at an appropriate level challenging and time consuming for CTSs and collaborative biostatisticians. In response to these challenges, the Biostats4You website was created, where existing online educational materials for a variety of statistical topics are vetted to identify those most appropriate for CTSs. In this manuscript, we describe the resource review process, provide information about statistical topics and resources currently available, and make recommendations for how CTSs and collaborative biostatisticians can utilize the Biostats4You website to improve training, mentoring, and collaborative research practices.

OBJECTIVES/GOALS: The overall goals of this project are: (1) to demonstrate the utility of staff with cross-disciplinary skills in biostatistics and epidemiology as program managers for CTSA BERD Cores, and (2) examine streamlined processes in project triaging, consultations, and data extractions for CTSA service requests when individuals are in this role. METHODS/STUDY POPULATION: Biostatistics and epidemiology are partnered disciplines incorporated in a variety of research areas, especially in human health sciences and health care. For interdisciplinary teams, including individuals trained in both specialties results in efficient research collaborations. When these individuals are program managers for research navigation, processes become more effective and expedited for project timelines and workflows across the CTSA. Examples of this integration are described in vignettes from project triaging, statistical consultations, and data extractions from CTSA service requests. Process comparisons of previous and current workflows are presented to show the advantages of utilizing this type of program manager within these areas of BERD. RESULTS/ANTICIPATED RESULTS: This project defines three areas of integration for a program manager with cross-disciplinary training in biostatistics and epidemiology: (1) project triaging to statistical teams, (2) study design and analysis consultations, and (3) data extractions. Each of these areas demonstrates prior processes that once would require multiple steps in CTSA service requests (including time, resources, and personnel) and identified common issues (slow response to requests, data re-extractions, and challenges providing statistical support) that are now avoided with a manager with cross-training in biostatistics and epidemiology methods. The advantages and disadvantages of integrating these individuals are also described. DISCUSSION/SIGNIFICANCE: BERD Cores seeking improvements to research navigation processes can gain efficiency by incorporating program managers with cross-disciplinary training in biostatistics and epidemiology. BERD Cores may also use this as a case study for translational science with innovation to longstanding challenges in CTSA research workflows.

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

Ongoing professional development is important for collaborative biostatisticians, as it enables them to remain current with the latest advances in statistical methodology and software, refine their analytical skills, and expand their domain knowledge, thereby facilitating their ability to contribute effectively to biomedical research. Although external opportunities for professional development, such as attending conferences and workshops, are widely recognized and valued in the field of biostatistics, there has been comparatively little attention given to internal opportunities for enhancing the skills and knowledge of biostatisticians which can be implemented with lower financial and time investment than external offerings. The purpose of this paper is to offer guidance for ongoing internal professional development activities that can be employed by collaborative biostatistics units in universities and academic medical centers to complement structured curricula and initial training. Specific examples of activities are provided so that collaborative biostatisticians and/or managers of biostatistical units can flexibly combine components to create an appropriately scaled, customized program that meets the needs of themselves or of the unit.

Background: A team of infectious diseases physicians, infectious diseases pharmacists, clinical laboratorians, and researchers collaborated to assess the management of lower respiratory tract infections (LRTIs). In 1 sample from our institution, 96.1% of pneumonia cases were prescribed antibiotics, compared to 85.0% in a comparison group. A collaborative effort led to the development of a protocol for procalcitonin (PCT)-guided antibiotic prescribing that was approved by several hospital committees, including the Antimicrobial Stewardship Committee and the Healthcare Pharmacy & Therapeutics Committee in December 2020. The aim of this analysis was to develop baseline information on PCT ordering and antibiotic prescribing patterns in LRTIs. Methods: We evaluated all adult inpatients (March–September 2019 and 2020) with a primary diagnosis of LRTI who received at least 1 antibiotic. Two cohorts were established to observe any potential differences in the 2 most recent years prior to adoption of the PCT protocol. Data (eg, demographics, specific diagnosis, length of stay, antimicrobial therapy and duration, PCT labs, etc) were obtained from the UK Center for Clinical and Translational Science, and the study was approved by the local IRB. The primary outcome of interest was antibiotic duration; secondary outcomes of interest were PCT orders, discharge antibiotic prescription, and inpatient length of stay. Results: In total, 432 patients (277 in 2019 and 155 in 2020) were included in this analysis. The average patient age was 61.2 years (SD, ±13.7); 47.7% were female; and 86.1% were white. Most patients were primarily diagnosed with pneumonia (58.8%), followed by COPD with complication (40.5%). In-hospital mortality was 3.5%. The minority of patients had any orders for PCT (29.2%); among them, most had only 1 PCT level measured (84.1%). The median length of hospital stay was 4 days (IQR, 2–6), and the median duration of antibiotic therapy was 4 days (IQR, 3–6). Conclusions: The utilization of PCT in LRTIs occurs in the minority of patient cases at our institution and mostly as a single measurement. The development and implementation of a PCT-guided therapy could help optimize antibiotic usage in patients with LRTIs.

Funding: No

Disclosures: None

The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.