Clinical and Translational Science trainees are motivated to publish influential research. However, the extent to which this work gains influence with the public is largely unknown.

The authors identified over 30,000 publications that received KL2 or TL1 grant support through a Clinical and Translational Science Awards hub, from 2006 through January 2024. The Altmetric Explorer database was then used, to collect references in sources such as news articles, tweets, and blogs. We investigated bibliometric characteristics and content areas, provide illustrative examples of influence, and determine the characteristics most likely to gain public attention.

Articles were published in 3,923 journals with a mean Journal Impact Factor (JIF) of 5.78, a mean Relative Citation Ratio (RCR) score of 2.02, and were cited an average of 33.7 times, totaling 1,017,291 citations. Over 4,800 were referenced in policy and were mentioned in over 64K news articles, 7K blog posts, and 480K X (Twitter) posts. The mean Altmetric Attention Score was 28.9, with 18.5% having scores of 20 or higher. Nearly 30% were related to COVID-19, indicating close public attention to this important health topic. Regression analyses indicate that higher JIF, being published after 2020, receiving more Mendeley downloads, higher RCR scores, being cited by in policy, and fewer academic citations, were more likely to receive altmetric attention.

By demonstrating how supported research has influence beyond academia to become “Academic Influencers,” this study represents a significant advance in our ability to evaluate translational research impact.

Biostatisticians increasingly use large language models (LLMs) to enhance efficiency, yet practical guidance on responsible integration is limited. This study explores current LLM usage, challenges, and training needs to support biostatisticians.

A cross-sectional survey was conducted across three biostatistics units at two academic medical centers. The survey assessed LLM usage across three key professional activities: communication and leadership, clinical and domain knowledge, and quantitative expertise. Responses were analyzed using descriptive statistics, while free-text responses underwent thematic analysis.

Of 208 eligible biostatisticians (162 staff and 46 faculty), 69 (33.2%) responded. Among them, 44 (63.8%) reported using LLMs; of the 43 who answered the frequency question, 20 (46.5%) used them daily and 16 (37.2%) weekly. LLMs improved productivity in coding, writing, and literature review; however, 29 of 41 respondents (70.7%) reported significant errors, including incorrect code, statistical misinterpretations, and hallucinated functions. Key verification strategies included expertise, external validation, debugging, and manual inspection. Among 58 respondents providing training feedback, 44 (75.9%) requested case studies, 40 (69.0%) sought interactive tutorials, and 37 (63.8%) desired structured training.

LLM usage is notable among respondents at two academic medical centers, though response patterns likely reflect early adopters. While LLMs enhance productivity, challenges like errors and reliability concerns highlight the need for verification strategies and systematic validation. The strong interest in training underscores the need for structured guidance. As an initial step, we propose eight core principles for responsible LLM integration, offering a preliminary framework for structured usage, validation, and ethical considerations.

Lack of reliable, affordable transportation is a common barrier to clinical research participation, potentially contributing to health disparities. Insufficient and/or nonexistent institutional policies on research-related transportation make it challenging for research teams to effectively overcome transportation barriers and promote research participation among people from disadvantaged backgrounds. This study’s goal was to review research-related transportation policies across clinical research-involved institutions and propose recommendations for what such policies should address to help promote research engagement among diverse, representative populations.

We surveyed 28 recruitment sites, members of the National Institutes of Health-funded Healthy Brain and Child Development Consortium, poised to recruit over 7000 families, and completed an online search for each site’s policies relevant to research-related transportation (i.e., transportation of participants or research staff travel to/from research activities). We identified, reviewed, and thematically described content of the relevant policies and developed summary recommendations for institutional guidance components.

We identified seven policies (from five sites) on research-related transportation; four provided guidance on research-related transportation services; two on reimbursement; and one on when research staff transports participants. The online search identified publicly available business travel policies for 22 sites. No policy addressed research staff travel specifically for “study business” or research personnel transporting children for research purposes.

Few institutions involved in clinical research have policies guiding research-related transportation. Such policies, if adopted, could help support research-related transportation and, thus, participation of individuals from disadvantaged backgrounds, increasing generalizability of research results and contributing toward reducing social and health disparities.

The hidden curriculum refers to the implicit norms and behaviors in academic environments that can particularly disadvantage scholars from backgrounds underrepresented in the scientific workforce (URSW). Critically, scientific mentors can support URSW mentees by making the hidden curriculum explicit to help these scholars navigate academia more effectively. However, mentors often lack the lived experience or training necessary to understand and fully address relevant hidden curriculum challenges.

We developed a set of 16 hidden curriculum competencies specifically for scientific mentors working with URSW mentees. A survey was conducted among diversity, equity, inclusion, and accessibility experts in translational science to assess the perceived importance of each competency. Their feedback was used to refine the final competencies, from which a conceptual framework was developed.

Survey results (n = 62) showed broad agreement on the competencies’ critical importance for mentoring across diversity, with several competencies, including identifying unconscious biases, acting as allies, and demystifying career pathways, receiving over 90% agreement for their importance for mentoring URSW mentees. Respondents from URSW backgrounds placed greater emphasis (p < 0.05) on several competencies, including understanding mentee perspectives, expanding professional networks, and allyship.

The 16 competencies, grouped into four domains (Foundation, Career, Science, and Overcoming Bias), collectively offer a comprehensive approach for mentors to build trust, support mentee career development, overcome practical barriers to mentee engagement in research, and actively combat bias. Our conceptual framework offers structured guidance for mentors and mentor training programs, identifying the skills needed to foster inclusive academic environments and enhance URSW retention and success.

A critical challenge for biomedical investigators is the delay between research and its adoption, yet there are few tools that use bibliometrics and artificial intelligence to address this translational gap. We built a tool to quantify translation of clinical investigation using novel approaches to identify themes in published clinical trials from PubMed and their appearance in the natural language elements of the electronic health record (EHR).

As a use case, we selected the translation of known health effects of exercise for heart disease, as found in published clinical trials, with the appearance of these themes in the EHR of heart disease patients seen in an emergency department (ED). We present a self-supervised framework that quantifies semantic similarity of themes within the EHR.

We found that 12.7% of the clinical trial abstracts dataset recommended aerobic exercise or strength training. Of the ED treatment plans, 19.2% related to heart disease. Of these, the treatment plans that included heart disease identified aerobic exercise or strength training only 0.34% of the time. Treatment plans from the overall ED dataset mentioned aerobic exercise or strength training less than 5% of the time.

Having access to publicly available clinical research and associated EHR data, including clinician notes and after-visit summaries, provided a unique opportunity to assess the adoption of clinical research in medical practice. This approach can be used for a variety of clinical conditions, and if assessed over time could measure implementation effectiveness of quality improvement strategies and clinical guidelines.

A promising approach to assessing research impact draws on the Translational Science Benefits Model (TSBM), an evaluation model that tracks the applied benefits of research in four domains: Clinical and Medical; Community and Public Health; Economic; and Policy and Legislative. However, standardized methods to verify TSBM benefit data, to aid in aggregating impact data within quantitative summaries, do not currently exist.

A panel of 11 topic experts participated in a modified Delphi process for establishing content and face validity of a set of criteria for verifying qualitative TSBM data. Two survey rounds were completed by panelists, with a moderated discussion in between rounds to discuss criteria not reaching consensus. Criteria with panel consensus at or above 70% in the survey rounds were confirmed as validated.

Criteria fell into 9 categories: Content Relevant, Project Related, Who, Reach, What, How, Novel, Documented Evidence, and When. The Delphi process yielded 197 total criteria across the 30 benefits characterized by the TSBM (range = 5–8 criteria per benefit).

The results of this Delphi process lay the foundation for developing a TSBM coding tool for evaluating and quantifying TSBM data. Standardizing this process will enable data aggregation, group analysis, and the comparison of research impact across contexts.

Community engagement in research (CEnR) is fundamental to recruitment and retention in research studies. CEnR study closure, with a view to promote subsequent interactions with participants, can foster long-term relationships between research teams and participants. We detail the principles, procedures and outcomes of respectful closure in a study focused on scaling-up tools to measure DNA integrity in population samples.

The study incorporated CEnR principles and practices, engaging a Community Advisory Board (CAB) to guide most study procedures. The CAB-designed closure protocol included 1) attempts at one-on-one contact via telephone, followed by a letter, if no contact was established; 2) provision of a study closure packet; 3) periodic mailing of study updates; and 4) a request for sustained interaction with the Community Engagement Team (CE Team), including participants’ approval to receive invitations for future projects. Items 3 and 4 were framed as choices to further interaction and its extent.

Among 191 participants enrolled, 119 were contacted at closure (62% retention rate). Most frequently (97.5%), contacted participants agreed to receive information about new research projects, while 90.8% agreed to receive ongoing information about the DNA integrity study. Subsequently, the CE Team implemented two study update mailings and two CEnR studies, enrolling 18 participants in a consultative role and four in a collaborative role.

Respectful study closure offers avenues for sustained interaction between CEnR teams and study participants, beyond the discrete boundaries of specific research projects. It can support the long-term connections that enable the positive outcomes of CEnR.