Objectives/Goals: Findings from cohort studies report a 1-year major adverse cardiac event (MACE) rate of 15% after vascular surgery. However, the real-world evidence (RWE) of the timing, incidence, and profile of these events is limited. We hypothesize that the MACE rate from RWE data will exceed current estimates. Methods/Study Population: Patients undergoing elective, intermediate- or high-risk vascular surgery at University of Maryland Medical System (1/1/2016—9/30/2025) were analyzed retrospectively. Data on six MACE outcomes (all-cause mortality, myocardial infarction, coronary revascularization, heart failure decompensation, cardiogenic shock, nonfatal cardiac arrest) were extracted from the Electronic Health Record utilizing algorithms incorporating ICD-10 codes, Current Procedural Terminology (CPT) codes, and clinical variables. We calculated the cumulative postoperative MACE incidence at 7-days, 30-days and 1-year following surgery. Results/Anticipated Results: We identified 9,490 patients of whom N=6,180 (65%) were male, N=6,327 (67%) were white, and N=2,755 (29%) were black or African American. We will describe baseline characteristics, comorbidities, pre-operative radiographic results and laboratory values for the study cohort. The primary endpoint is cumulative MACE incidence at 30-days. Secondary endpoints include composite MACE incidence at 7-days and 1-year, as well as outcomes stratified by individual MACE parameters. Discussion/Significance of Impact: This retrospective study aims to provide a detailed characterization of MACE following vascular surgery. The derived data are anticipated to inform pre-operative risk stratification for real-world scenarios and in doing so identify opportunities for preventative interventions targeting MACE.

Objectives/Goals: To develop a tool to assess mentoring relationships (mentor-mentee dyad) that can be used to monitor relationships over time to identify those at-risk along with knowledge and skills gaps for targeted coaching and career development initiatives and to evaluate mentoring programs. Methods/Study Population: Informed by literature review and faculty input, the CCTSI mentoring relationship assessment tool consists of parallel mentor and mentee questionnaires [applewebdata%3A//6E5CC409-7F40-4414-AF57-C97DF94108CD#_msocom_1]; requesting both a self- and partner-assessment. Assessments are completed upon K or T award initiation, and then every 9-12 months to characterize changes throughout the course of these CCTSI programs that include structured mentored training. Reports are generated for each mentor-mentee dyad as well as aggregate reports of mentors/mentees over time. After pilot testing with CCTSI K and T awardees, the tool was revised and then integrated into the K and T programs. Results/Anticipated Results: Principal component analysis of pilot data from 30 K and T awardees revealed that 5 questions be removed. The revised tool consists of 3 sections: assess the mentor (14 items), rate the relationship (9 items), and self-assessment (20 items for mentees, 16 items for mentors). The revised tool was completed by 12 T and 6 K mentee-mentor dyads. T dyad reports revealed 3 dyads at-risk. Aggregated data for T mentors and mentees identified areas for targeted intervention for expectation alignment, review/revise IDP, and sponsorship activities. Aggregated K data revealed that mentees rated their mentors more favorably than mentors’ self-assessments; discrepancies were noted related to career development. K mentees’ ratings corresponded to their mentors’ rating except for mentees’ ability to review/revise IDP. Discussion/Significance of Impact: The CCTSI mentoring relationship assessment tool demonstrates promise in identifying at-risk mentor-mentee relationships and for identifying knowledge/skills gaps for targeted career development initiatives. Future work will determine its usefulness for evaluating mentoring programs and capturing changes in relationships over time.

Objectives/Goals: To identify real-world type 2 diabetes (T2D) treatment pathways and evaluate whether AI models trained on electronic health record (EHR) medication histories can improve prediction of glycemic outcomes in routine clinical practice. Methods/Study Population: We conducted a retrospective cohort study using de-identified EHR data from the TriNetX Research Network (>3 M adults with T2D, 2019–2024). Medication, laboratory, and demographic data were standardized in PostgreSQL on the University of Utah CHPC. Patients with T1D, pregnancy, or incomplete follow-up were excluded. Longitudinal medication sequences were clustered using hierarchical agglomerative methods to identify treatment trajectories. Transformer-based models incorporating full prescription histories were trained to predict 6-month HbA1c, and performance (RMSE) was assessed overall and across demographic groups. Results/Anticipated Results: Among >3 M adults with T2D, we identified 40 distinct treatment clusters representing mono-, dual-, complex-, GLP-1–based, and variant pathways. GLP-1/GIP agents were typically used as 2nd–3rd-line therapy after metformin or SGLT2i and produced the largest BMI and HbA1c improvements. Baseline XGBoost models using EHR features predicted 6-month HbA1c with RMSE ≈ 0.72. The last HbA1c value was the most predictive feature, though additional labs and demographics modestly improved accuracy. Ongoing work is developing transformer models to capture longitudinal patterns and medication history effects. Discussion/Significance of Impact: National EHR networks can support development of trustworthy, fairness-aware AI to model diabetes pharmacotherapy. Insights reveal disparities in advanced therapy uptake and inform equitable decision-support design.

Objectives/Goals: Building on Schneider et al., which mapped translational science (TS) principles across urban CTSA hubs, evaluators and one pilots administrator within the Consortium of Rural States CTSAs examined how hubs prioritize these principles. This work can ensure shared TS values and inform a common rubric across rural CTSA institutions. Methods/Study Population: We analyzed CORES ranking data from eight CTSA hubs (n = 49 respondents). Item-level mean ranks and standard deviations summarized TS principle priorities, while respondent-averaged means reduced bias from unequal item counts. Site-level profiles were compared using Spearman rank correlations to examine cross-hub similarity. Results/Anticipated Results: Our analysis of CORES data (8 hubs, n = 49) replicates Schneider et al.’s finding that TS solutions center on generalizable solutions and efficiency/speed. Rural hub rankings showed tight agreement and low dispersion, confirming these as shared core TS values. Despite differing experimental and hub contexts, the pattern mirrors Schneider et al. (2024) and strengthens support that NCATS’ TS principles apply consistently across both urban and rural CTSA hubs. Discussion/Significance of Impact: CORES hubs prioritized Translational Science (TS) projects emphasizing generalizable solutions and efficiency, reflecting shared values. Findings align with prior TS/TR work and support creating a concise, qualitative TS rubric to guide co-funded projects with system-level impact.

Objectives/Goals: The endocannabinoid (eCB) system is integral to pain perception and may have antinociceptive effects. This study is the first to examine sex differences in eCB levels and their associations with pain and risk factors that may contribute to the development or exacerbation of pain symptoms, such as sleep disturbances. Methods/Study Population: Data were analyzed from the Adolescent Brain Cognitive Development StudySM, which included a substudy to measure circulating blood eCB concentrations in a subset of participants (n=403, mean age=12.56±1.05 years, 46.4% female). Covariates included age, race and ethnicity, puberty, sex, BMI, fasting state, and recent exercise. Pain was assessed on day of the blood draw (0–10 scale), and sleep disturbances were measured by the number of nighttime awakenings. Five eCB and eCB-like molecules were examined: anandamide (AEA), 2-arachidonoylglycerol (2-AG), palmitoylethanolamide (PEA), oleoylethanolamide (OEA), and 2-oleoylglycerol (2-OG). Results/Anticipated Results: There was a significant sex difference in 2-AG concentrations (β=-5.36, CI [-8.53–2.18], p-adj=0.005), with female youth having lower concentrations than males. Because of this sex difference, all other analyses were sex-stratified. There was no association between pain on the day of the blood draw and eCB concentrations in either sex. Male youth alone exhibited a positive correlation between nighttime awakenings and concentrations of AEA (β=0.62, CI [0.26–0.99], p =0.001) and OEA (β=0.14, CI [0.065–0.21], p<0.001). Discussion/Significance of Impact: These findings suggest there may be sex-specific relationships with the eCB system and risk factors for pain. Future analyses will leverage longitudinal eCB measurements and examine how changes in eCB may relate to pain and sleep problems during adolescence.

Objectives/Goals: Post-stroke fatigue (PSF), marked by early exhaustion and reduced activity tolerance, affects over half of stroke survivors. This novel study used All of Us Research Program data to explore factors linked to PSF, addressing gaps left by prior studies with methodological limitations. Methods/Study Population: We conducted a cross-sectional analysis of ischemic stroke survivors from the All of Us Research Program Controlled Tier dataset. Participants were identified using ICD-10 diagnosis I63 for cerebral infarction, with strokes occurring prior to survey completion. Self-reported fatigue was assessed using a 5-point Likert scale (none to very severe). Demographics, pre-stroke comorbidities, and health status factors were extracted from electronic health records and surveys. Proportional odds regression models identified factors associated with higher fatigue levels. Results/Anticipated Results: 4,009 ischemic stroke survivors met inclusion criteria (mean age 64±13 years, 53% female, median 2 years post-stroke). Fatigue was documented in 56% of participants through electronic health records, while 84% reported some level of fatigue on surveys. In multivariable analysis, factors significantly associated with greater fatigue included younger age (OR: 0.98, 95% CI: 0.98–0.99, p<0.001), female sex (OR: 1.38, 95% CI: 1.21–1.57, p<0.001), current smoking (OR: 1.19, 95% CI: 1.05–1.36, p=0.008), diabetes mellitus (OR: 1.34, 95% CI: 1.1–-1.54, p<0.001), chronic obstructive pulmonary disease (OR: 1.37, 95% CI: 1.16–1.62, p<0.001), and mood disorders (OR: 1.96, 95% CI: 1.72–2.23, p<0.001). Discussion/Significance of Impact: This is among the first studies to examine PSF using large, representative datasets. With 84% reporting symptoms, findings reveal under recognition in clinical settings and identify modifiable risk factors to guide targeted, evidence-based interventions.

Objectives/Goals: There is growing interest in using physiological data produced from medical devices such as 12-lead electrocardiographs within the Observational Health Data Sciences and Informatic community. We have introduced new waveform extensions to support this need and illustrate its implementation using 12-lead ECG. Methods/Study Population: We identified 8,822 12-lead ECGs from Tufts Medical Center between January 1, 2018 and April 31, 2024. 6,702 of these ECGs were linked to patients in our Observational Medical Outcomes Partnership (OMOP) Research Data Warehouse (TRDW). The cohort of patients with linked ECGs were extracted into a study OMOP database in which the new Waveform Extension (WFExt) were implemented. A data extract/transform/load import process was developed that complied with the WFExt specifications and formed a new custom vocabulary and concepts to support waveform data that is not presently supported by standard vocabularies such as SNOMED and LOINC. Results/Anticipated Results: Using the OMOP Waveform Extensions allowed us to capture the full range of high-resolution physiological data contained in 12-lead electrocardiograms. The OMOP common data model is designed to capture low-resolution data contained in electronic health record systems. In emergency medicine, critical care, and neonatal care, medical devices play an important role in driving treatment decisions. Research therefore needs access to both clinical and medical device data in developing clinical decision support tools. In this project, we demonstrate the ability to connect clinical data such as outcomes in an OMOP database with device measurements, computerized interpretations, metadata, and provide linkage to the source device record to support signal processing and new algorithm development using the WFExt. Discussion/Significance of Impact: The OMOP WFExt mirrors work to support imaging data, and both extend the utility and research capacities among the many Clinical Translational Science Institutes that have already implemented an OMOP-based research warehouse. The WFExt is a standards-based and open science approach to support research and backed by a worldwide OMOP community.

Objectives/Goals: Medical mistrust is a persistent challenge to effective healthcare delivery and disproportionately affects Black, Indigenous, and other people of color (BIPOC). The relationship between religiosity and patient trust is not well understood but can provide key insight into improving primary care. Methods/Study Population: This study examines the association between religiosity and trust among BIPOC and White patients receiving primary care at Tufts Medical Center. Eligible participants will be adults aged ≥18 years without cognitive impairment. Recruitment will occur via telephone and during office visits. We will create surveys measuring religiosity and trust in primary care physicians (PCPs), and mean scores will be compared across subgroups, including race, sex, age, and religious affiliation. We will also conduct interviews to explore patient perspectives on PCP characteristics that encourage or discourage trust, as well as the role of religion in shaping trust within primary care settings. Results/Anticipated Results: Based on prior literature, we hypothesize that BIPOC patients will have higher mean scores of mistrust than White patients. We also hypothesize a statistically significant interaction between religiosity and trust for both BIPOC and White patients, such that higher religiosity scores will be associated with greater levels of mistrust. In subgroup analyses, we hypothesize that Black/African American and Hispanic patients will have the strongest moderating effect between religiosity and mistrust. We further expect patients who report high levels of trust in their PCPs to identify physician qualities including perceived expertise, empathy, cultural humility, and shared decision-making. Discussion/Significance of Impact: Our results will inform the development and implementation of a medical mistrust screening tool that measures individual rather than broader group-based perceptions of trust – a tool that is not yet widely integrated into primary care – and can enhance physicians’ approach to patient-centered care.

Objectives/Goals: This survey engages the research professional (RP) workforce in assessing key employee engagement drivers. The survey (goal n = 500) evaluates seven indicators across four themes. These data will identify high-impact needs and retention gaps for RPs at various career stages. Methods/Study Population: This enterprise-wide survey is Phase II of a multiyear employee experience initiative for RPs who are widely defined as university staff who support research activities impacting human health. This second-phase builds on year-one HR data analysis and employee focus groups that identified key workforce needs and organizational effectiveness gaps. The mixed methods survey design leverages peer-reviewed employee engagement models from business management and organizational effectiveness literature, including the widely cited Job demands-resources (JD-R) model (Bakker, et. Al 2007). Survey question types include close-ended, categorical, Likert scale, and free text. Results/Anticipated Results: Survey data will translate workforce-wide perspectives and experiences into actionable strategies across seven drivers of engagement. To facilitate effective dissemination of findings to stakeholders, the drivers are arranged into four themes: Sense of Place: * Community & Connection * Psychological Safety Working with Purpose: * Employee Accomplishment & Achievement * Career Navigation Effective Processes: * Procedural Establishment & Accountability * Job Clarity & Workload Sustainability Research & Institutional Pride: * Work Significance & Impact Data will also create a current-state workforce snapshot including hybrid-work arrangements, key motivators, and leadership opportunity preferences. Discussion/Significance of Impact: This survey will create a robust dataset that will provide insights of workforce trends, operational gaps, and necessary areas of support. Organizational change management best practices and user-centered design approaches will facilitate an action-oriented dissemination plan.

Objectives/Goals: Test whether early childhood attachment security relates to epigenetic aging during adolescence, evaluate consistency across clocks, and examine heterogeneity for targeting. Methods/Study Population: Adolescence is a high-stakes period for stress biology. DNA methylation clocks condense CpG information into indices of cellular and system-level aging that relate to stress load and risk profiles. This study examines a population-based cohort with saliva DNA methylation at ages 9 and 15 and 5 clocks. Prespecified longitudinal regression and ANCOVA assess associations and incremental value beyond age 9 clocks. Models adjust for prespecified social and technical covariates; covariate missingness is handled with multiple imputation. False discovery rate control is applied within each wave. Sensitivity checks vary covariates and scaling. Results/Anticipated Results: Findings show directionally consistent protective signals linking early attachment security to youth DNA-based aging. The pattern concentrates by age 9, guiding precision child health efforts toward early relational supports and low-burden, saliva-based screening that is feasible in diverse community and clinical settings. Discussion/Significance of Impact: Results support a pathway to intervention. Attachment screening paired with saliva methylation clocks can identify who benefits from relational supports, when to intervene, and how to set decision thresholds. The approach fits pediatric workflows, guides stratification in attachment-focused trials, and is scalable to external validation.

Objectives/Goals: Mayo Clinic launched the Community Health Assessment and Improvement Measures Program (CHAMP) Awards to support community–academic partnerships that address community health needs through community-engaged research. This abstract describes the development, implementation, and impact of CHAMP. Methods/Study Population: Launched in 2014, CHAMP offers two awards: Research Awards for discreet community-engaged research projects and Partnership Development to develop infrastructure for future community-engaged research. Awardees must address identified community health needs and involve community partners. The awards span two years; Year 1 focuses on partnership development and Year 2 on implementing project aims. Projects are reviewed and scored by internal investigators and community partners, and top-scoring proposals are selected by a review committee. Budgets must support community partners and teams submit progress and final reports to track the impact of the awards. Outcomes such as grants, publications, and career advancement are tracked by program staff. Results/Anticipated Results: The CHAMP Awards have completed seven funding cycles, supporting 28 community–academic projects and partnerships across Arizona, Florida, and the Midwest. To date, 23 awardees have published over 250 peer-reviewed articles related to community-engaged research. Of the 28 projects, over 60 subsequent grants have been secured from agencies such as the National Institute of Health, National Cancer Institute, and others, expanding both impact and sustainability. Project topics include school-based vaccinations, cardiovascular disease prevention, cancer advocacy, screening, and diabetes management. Awardees and partners continue to advance science, build lasting relationships, and secure additional resources. Discussion/Significance of Impact: The CHAMP Awards advance community-engaged research by developing and funding strong community–academic partnerships that address identified community health needs. The program supports early-career investigators, promotes workforce development, and has catalyzed new funding through subsequent grants.

Objectives/Goals: Blood pressure (BP) control remains suboptimal among US patients with hypertension. Single-pill combination (SPC) therapies are commonly used to improve adherence; however, their effectiveness for achieving early and sustained intensive BP control is unclear. Methods/Study Population: We performed a post hoc analysis of SPRINT including 2,736 participants propensity matched in 1:2 ratio to compare effects of SPCs with equivalent multi-pill therapy. The estimated marginal odds of achieving optimal BP control were derived using generalized linear mixed models with repeated measures (LMMRM). The association between time-updated SPC use and BP change in short- (≤6 months) and long-term (>6 months) follow-up was assessed with LMMRM and SPC*time interaction term. Multivariable Cox models evaluated association of SPC use with CV events and serious adverse events (SAEs). Results/Anticipated Results: Among SPRINT participants (N=8623), 9.3% (N=803) were prescribed SPC at baseline with greater use in the intensive vs. usual care group (5.79 vs. 3.90 per 100 person-months; p-diff<0.001). Among matched pairs (SPC[n=912); multi-pill therapy[n=1824]), SPC use was associated with 22% increased likelihood of achieving target BP by 6 months [OR(95% CI): 1.22(1.05, 1.42)]. Participants receiving SPCs (vs multi-pills) experienced more rapid BP reduction in the first 6 months (-2.0 vs. -1.2 mmHg monthly change; p-diff<0.001). Over long-term follow-up, participants using SPCs achieved significantly lower SBP at each timepoint. The risk of the primary CV composite endpoint and SAEs was not significantly different between groups. Discussion/Significance of Impact: SPC therapy resulted in more rapid and sustained BP reduction and a greater likelihood of achieving BP control compared with multi-pill therapy without increase in SAEs. Future research is needed to identify optimal strategies for implementing SPC-based approaches at the population-level and optimize public health benefits of intensive BP control.

Objectives/Goals: The objective of this study was to evaluate the performance of multimodal machine learning (ML) models trained to predict differentiated thyroid cancer (DTC) recurrence using clinical data combined with novel natural language processing (NLP) derived features extracted from patient cytopathology and surgical pathology reports. Methods/Study Population: This was a retrospective study of adult thyroid cancer patients treated at an academic medical center. Patients were classified as having cancer recurrence or no recurrence. NLP features were extracted from cytopathology and surgical pathology reports using Term Frequency–Inverse Document Frequency (TF-IDF), latent Dirichlet allocation (LDA), and a zero-shot large language model (LLM) classification. 5 multimodal ML models were trained to predict cancer recurrence utilizing a combination of NLP and LLM features and clinical variables. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC) and precision recall area under the curve (PR-AUC). The top performing model was optimized with a 5-fold cross-validation. Feature importance was calculated. Results/Anticipated Results: 480 patients with differentiated thyroid cancer diagnosed on surgical pathology were included in this study. The baseline model (clinical variables only) had a F1-score of 0.52 and an AUC of 0.53. The optimized gradient boosting model utilizing all features (EMR, LDA, TF-IDF, and LLM) had a F1-score of 0.87 and an AUC of 0.86. Topic words and themes from the patient cytopathology and surgical pathology reports were generated using LDA. Topic themes in cytopathology reports include malignancy, lymph node evaluation, and molecular testing. Topic themes in surgical pathology reports include histologic subtype, orientation of nodule, and intraoperative biopsy. The LDA themes of malignancy and histologic subtype ranked the highest in terms of feature importance. Discussion/Significance of Impact: Multimodal models utilizing novel NLP features derived from unstructured pathology reports may enable improved prediction of recurrence in patients with DTC. Our optimized model demonstrated that 4 of the top 6 highest features were LDA topics. Topic modeling may be a valuable tool to extract relevant information from unstructured clinical notes.

A gap exists in the UK in recognising, understanding and meeting the mental health needs of refugee women in the perinatal period who have experienced war and conflict. Women survivors of war who have been exposed to conflict-related gender-based violence have increased vulnerabilities during pregnancy.

Objectives/Goals: To advance the clinical utility of a risk calculator for identifying young children’s mental health risk, we: 1) replicate an established mental health risk algorithm in two toddler-preschool samples; 2) determine added predictive value of child and parenting assets, advancing a strengths-based approach; and 3) discuss next steps for implementation. Methods/Study Population: Data were from two studies: A population-based Cohort 1 (N=2,763) and an independent risk-enriched Cohort 2 (N=323). In Cohort 1, children (48% girls) were 51.9% Black, 21.4% White, and 2.7% Other Race; 23.8% were Hispanic. In Cohort 2, children (44% girls) were 49.2% White, 38.1% Black, and 8.4% Other Race; 11.1% were Hispanic. Risks and assets were assessed in toddlerhood/early-preschool, and psychopathology was measured later in preschool. Epidemiologic risk prediction methods were applied to: 1) replicate the published risk model that includes demographics, irritability, and adverse childhood experiences (ACEs); 2) examine added predictive utility of child and parenting assets. Predictive utility was based on area under the curve (AUC) and/or the integrated discrimination improvement (IDI). Results/Anticipated Results: The previously published risk algorithm that includes demographics, child irritability, and child ACEs was replicated in both cohorts (AUC=.70 for both; IDI=.07 in Cohort 1 and .06 in Cohort 2). Via the IDI, there was added predictive utility of child assets (i.e., social competence) in both cohorts (IDI=0.008 in Cohort 1 and 0.02 in Cohort 2), as well as added predictive utility of parenting assets (i.e., parenting involvement/self-efficacy) in Cohort 1 (IDI=0.004). Preliminary evidence for barriers/facilitators regarding early childhood mental health screening in pediatric primary care and preschool settings will also be discussed, as part of a roadmap for future implementation of the calculator in routine care. Discussion/Significance of Impact: Improving early mental health risk algorithms through a strengths-based lens is essential for evidence-based and equitable decision-making. We have laid the groundwork for future implementation of a mental health decision tool in routine care of young children, from pediatric primary care to preschool.

Objectives/Goals: As the field of implementation science (IS) enters its “teenage” years, most published examples describing capacity-building efforts within the field rely on quantitative methods and are retrospective. Very few of these studies engage in prospective, rigorous qualitative research to assess the needs of their local networks in building IS capacity. Methods/Study Population: Individual, semi-structured interviews were conducted with local investigators who had indicated prior interest in implementation science research and had been engaged with local CTSA resources at varying levels. The interview guide was informed by the Consolidated Framework in Implementation Research (CFIR) domains, and interviews were conducted via Zoom. Interviews were mostly hand transcribed, while the Zoom Transcribe feature was used for some interviews. Transcribed interviews were uploaded into Dedoose 10, and an inductive, iterative coding approach was taken for analysis. This study received exempt IRB approval from the University of Kansas Medical Center. Results/Anticipated Results: A total of seven faculty members, spanning the early to late career stages, participated in the interviews. They represented three of the major research institutions in the network, and the interviews lasted 24 to 58 minutes. The themes that emerged related to facilitators included mentorship, feedback opportunities, and dedicated funding. Themes related to barriers included a lack of methodological support, limited opportunities for collaboration, and a perceived exclusivity in the field of implementation science. From these, three recommendations were given: provide a dedicated IS funding opportunity locally, facilitate mentorship and collaborations, and promote leadership inclusiveness. Discussion/Significance of Impact: The study findings directly influenced the development of IS activities within the CTSI renewal. By applying IS’s best practices, utilizing qualitative assessments of implementation science networks can help the field address specific gaps in capacity building and respond more effectively to the needs of our investigators.

Objectives/Goals: The goal of this project is to integrate human and animal data to explore the relationship between social participation and cognitive functioning after traumatic brain injury (TBI). Further, we aim to determine whether reduced social participation is not only a consequence of TBI but a potential driver of post-injury cognitive deficits. Methods/Study Population: This study uses a translational framework to examine how social participation influences cognitive outcomes after traumatic brain injury (TBI) using complementary human and animal models. Longitudinal TBIMS data will be analyzed to assess associations between social participation (PART-O: productivity, out and about, social relations) and cognition (BTACT: memory, fluency, reasoning, processing speed) using linear regression (potentially a Random Intercept Cross-Lagged Panel Model to capture bidirectional effects over time). Further, mice with mild, moderate, or severe TBI will be housed in isolated, standard, or enriched social environments. Post-injury cognitive performance (NOR, Y-maze) and neuronal proliferation and survival in the prefrontal cortex and hippocampus will be evaluated. Results/Anticipated Results: We anticipate that both humans and animals with greater social participation will demonstrate superior cognitive performance over time. In the animal model, we further expect that mice housed in social or enriched environments will exhibit reduced cell death and enhanced neuronal survival in the prefrontal cortex and hippocampus compared to socially isolated counterparts. Discussion/Significance of Impact: Together, these findings may inform future rehabilitation strategies that integrate social participation as a core component of cognitive recovery supporting more personalized and multidisciplinary approaches to chronic TBI care.

Objectives/Goals: In 2021, Mayo Clinic Center for Clinical and Translational Science (CCaTS) launched CE Studios to enhance community-informed research across a multisite institution. This study applied the Proctor’s implementation outcomes framework to evaluate the implementation efforts across diverse settings. Methods/Study Population: From June 2022 to October 2025, we held 59 CE Studios across 19 sites in Arizona, Florida, and Midwest. CE Studios were done in-person and virtually via Zoom. Researchers and community members completed a survey assessing their experience and perceived influence of communities’ feedback on the research project. Data were collected and analyzed from June 2022 to October 2025. To assess the implementation, we applied Proctor’s Implementation Outcomes Framework, which examines eight constructs: Acceptability, Adoption, Appropriateness, Feasibility, Fidelity, Implementation Cost, Penetration, and Sustainability. Results/Anticipated Results: Thirty (51%) researchers and 286 (67%) community members completed the survey. Both groups indicated high satisfaction with CE Studio planning, format, and facilitation, emphasizing meaningful contribution of community feedback on research. All researchers noted at least one stage of their research was influenced by community input: pre-research (28%), study design (28%), implementation (22%), infrastructure (15%), analysis (1%), and dissemination (6%). Over time, CE Studios requests increased, reflecting recognition of their value in enhancing research. The framework revealed missing structured coordinators’ feedback, a key for understanding implementation and highlighted the need to revise evaluation tools and identify additional data sources aligned with the broader framework. Discussion/Significance of Impact: Applying the Proctor’s Implementation Outcomes Framework revealed areas to strengthen CE Studio implementation such as coordinator feedback, refinement of evaluation tools, data source, and strategies to assess CE Studio sustainability and institutional impact. These insights offer a model to guide similar efforts at other institutions.

Objectives/Goals: Emerging studies demonstrate association of hearing loss with adverse mental and cognitive outcomes, warranting the need for reliable measures of hearing in epidemiological research. This study compared the prevalence of hearing loss among US adults based on objective and self-reported measures, as well as hearing aid use from the HRS. Methods/Study Population: This is a cross-sectional study utilizing data from the 2016/18 Health and Retirement Study (HRS), a nationally representative cohort of US adults aged ≥50 years. Participants who completed both the objective hearing screening using the HearCheck device and hearing-related questionnaires were included (n=13,087). Survey weights were applied to account for the complex sampling design to estimate the prevalence of hearing loss as well as hearing aid use among those with objective hearing loss. Agreement between objective and self-reported hearing status was assessed using Cohen’s κ. Multivariable regression analyses examined demographic factors (age, sex, race/ethnicity, income, and education) associated with under-reporting of hearing loss and hearing aid use. Results/Anticipated Results: In this nationally representative sample of US adults aged ≥50 years, the prevalence of objective hearing loss was 52.6% [95% CI: 51.5-53.7] and self-reported hearing loss was 26.9% [25.9-27.9%]. Hearing aid use rate was 9.7% [8.9-10.5] among those with any objective hearing loss (score<6) and 44.9% [41.1-48.9] among those with moderate or worse hearing loss (score<3). Agreement between objective and self-reported hearing was low (Cohen’s κ=0.2), with 61.6% [60.1-63.0] under-reporting their hearing loss. In a multivariable model accounting for demographic factors, under-reporting was more likely among females (OR: 1.8 [1.6-2.1]) and individuals with higher education (OR: 2.0 [1.6-2.6]). Hearing aid use was associated with higher education (OR: 2.9 [2.0-4.2]) and higher income (OR: 2.8 [1.9-4.0]). Discussion/Significance of Impact: Hearing loss is common in older US adults, yet hearing aid use remains low. Frequent under-reporting of hearing loss highlights the need for objective measures in epidemiology studies to elucidate the impact of hearing loss on health outcomes. Addressing costs and awareness may improve access and lessen negative health effects related to hearing loss.

Objectives/Goals: This poster overviews the national landscape of policies related to patient engagement for research known as opt-in or opt-out policies. The evaluation compares policy types, changes to those policies, managing institutional hurdles, the resources used to support patient engagement for research, and shares best practices. Methods/Study Population: We identified peer academic medical institutions with active CTSA funding from public records and invited the contact PI of 37 institutions to speak about their patient engagement policies. We connected with 26 of these institutions in 30–60 minute discussions with members of CUIMC, WCM, and NYP. Each participant consented to being recorded for note-taking purposes before sharing an open-ended narrative about their patient engagement policies. Questions were completed throughout this process to address policy specifics, institutional resources, ethical considerations, and patient engagement. Results/Anticipated Results: In speaking with 26 peer institutions, we identified 50% have continuously operated under an opt-out policy with an additional 25% of institutions having previously switched from opt-in policies to opt-out. Those having undergone a policy switch commonly did so in alignment with an institutional implementation of an EHR system. The majority of opt-out institutions had special populations that were excluded from this policy for a multiple of ethical and practical considerations. Institutional decisions on policy type often centered on patient needs and experience, weighing patient autonomy versus patient privacy. Discussion/Significance of Impact: There has been a national shift toward opt-out patient engagement policies, frequently aligning with the launch of an EHR system. Those switching to opt-out policies often cited patient autonomy as their guiding ethical principle and chose to engage with patient advocacy groups following the policy implementation for operational optimization.