Clinical/translational science (CTS) is team-based, requiring effective collaboration and communication across many disciplines involving a variety of stakeholders. We implemented a pre-doctoral team-based training model with didactic and experiential curricular interventions to support the development of CTS research skills in a cross-disciplinary team environment. We assessed the potential impact of this new training model as a team science intervention that can catalyze new cross-disciplinary collaborations across the institution.

Between 2016 and 2020, 32 pre-doctoral students and 26 co-mentors participated in the assessment of the CTS Team program over a two-year period of TL1 training grant support. Data collection and analyses followed a program logic model and used a variety of metrics for clinical and translational scientist career success.

CTS training in the context of CTS Teams supported improved self-efficacy for clinical research skills and resulted in a significant increase in the frequency of participation in cross-disciplinary collaborative activities by both trainees and mentors. Most CTS Team co-mentor pairs had not previously collaborated. Two-thirds of the co-mentors plan to continue collaborating, and most (85%) currently use or plan to use collaboration tools, for example, written collaboration plans, authorship agreements.

The CTS Team training model provides a unique clinical and translational science team training experience that embeds authentic cross-disciplinary research collaboration into PhD research projects. It establishes trainee cohorts that are diverse in terms of scientific disciplines and translational research phases, and creates a new cross-disciplinary community of practice across faculty members and research groups in multiple colleges.

Choosing an appropriate electronic data capture system (EDC) is a critical decision for all randomized controlled trials (RCT). In this paper, we document our process for developing and implementing an EDC for a multisite RCT evaluating the efficacy and implementation of an enhanced primary care model for individuals with opioid use disorder who are returning to the community from incarceration.

Informed by the Knowledge-to-Action conceptual framework and user-centered design principles, we used Claris Filemaker software to design and implement CRICIT, a novel EDC that could meet the varied needs of the many stakeholders involved in our study.

CRICIT was deployed in May 2021 and has been continuously iterated and adapted since. CRICIT’s features include extensive participant tracking capabilities, site-specific adaptability, integrated randomization protocols, and the ability to generate both site-specific and study-wide summary reports.

CRICIT is highly customizable, adaptable, and secure. Its implementation has enhanced the quality of the study’s data, increased fidelity to a complicated research protocol, and reduced research staff’s administrative burden. CRICIT and similar systems have the potential to streamline research activities and contribute to the efficient collection and utilization of clinical research data.

Essential trace elements and micronutrients are critical in eliciting an effective immune response to combat sepsis, with selenium being particularly noteworthy. The objective of this investigation is to analyze and the levels of serum selenium in neonates within sepsis and control groups.

In 2023, a case–control study was carried out involving 66 hospitalized infants – 33 diagnosed with sepsis forming the case group and 33 free from sepsis constituting the control group – along with their mothers, at Children’s and Shariati Hospitals in Bandar Abbas. The serum selenium concentrations (expressed in micrograms per deciliter) were quantified utilizing atomic absorption spectrometry. Subsequently, the data were processed and analyzed using IBM SPSS statistical software, version 22.

The average serum selenium level in neonates with sepsis (42.06 ± 20.40 µg/dL) was notably lower compared to the control group (55.61 ± 20.33 µg/dL), a difference that was statistically significant (p-value = 0.009). The levels of serum selenium were comparable between neonates and mothers across both study groups.

The findings of this research indicate that selenium levels in the sepsis group were reduced compared to the control group, despite similar selenium levels in the mothers and neonates in both groups, suggesting that sepsis could be associated with a decrease in selenium levels.

High impact biomedical research is increasingly conducted by large, transdisciplinary, multisite teams in an increasingly collaborative environment. Thriving in this environment requires robust teamwork skills, which are not acquired automatically in the course of traditional scientific education. Team science skills training does exist, but most is directed at clinical care teams, not research teams, and little is focused on the specific training needs of early-career investigators, whose early team leadership experiences may shape their career trajectories positively or negatively. Our research indicated a need for team science training designed specifically for early-career investigators.

To address this need, we designed and delivered a 2-day workshop focused on teaching team science skills to early-career investigators. We operationalized team science competencies, sought the advice of team science experts, and performed a needs assessment composed of a survey and a qualitative study. Through these multiple approaches, we identified and grouped training priorities into three broad training areas and developed four robust, hands-on workshop sessions.

Attendees comprised 30 pre- and post-doc fellows (TL1) and early-career faculty (KL2 and K12). We assessed impact with a pre- and post-workshop survey adapted from the Team Skills Scale. Results from the pre- and post-test Wilcoxon signed-rank analysis (n = 25) showed statistically significant improvement in team science skills and confidence. Open-ended responses indicated that the workshop focus was appropriate and well targeted to the trainees’ needs.

Although team science education is still very much in its infancy, these results suggest that training targeted to early-career investigators improves team skills and may foster improved collaboration.

We describe the effectiveness of community outreach and engagement in supporting recruitment for the US National Children’s Vanguard Study between 2009 and 2012.

Thirty-seven study locations used 1 of 4 strategies to recruit 18–49-year-old pregnant or trying to conceive women: (1) Initial Vanguard Study used household-based recruitment; (2) Direct Outreach emphasized self-referral; (3) Enhanced Household-Based Recruitment enhanced Initial Vanguard Study strategies; and (4) Provider-Based Recruitment recruited through healthcare providers. Outreach and engagement included advance letters, interactions with healthcare providers, participation in community events, contacts with community organizations, and media outreach.

After 1–2 years, 41%–74% of 9844 study-eligible women had heard about the National Children’s Vanguard Study when first approached. Women who heard were 1.5–3 times more likely to consent. Hearing via word-of-mouth or the media most frequently predicted consent. The more sources women heard from the higher the odds of consent.

We conclude that tailored outreach and engagement facilitate recruitment in cohort studies.

Rural communities make up 19% of the US population, yet are underrepresented in clinical trials. Community engagement methods can facilitate collaboration and trust with local healthcare personnel to enhance enrollment. The purpose of this manuscript is to describe community engagement methods and their impact on enrollment in a pragmatic clinical trial.

We describe a variety of methods used in the Fibromyalgia TENS in Physical Therapy Study (FM-TIPS) to enhance enrollment in rural communities and low-enrolling clinics. Community engagement methods were implemented partway through the trial for selected groups: Targeted Rural (TR) (n = 10), Targeted Low Enrolling (TLE) (n = 6), and compared to Untargeted Groups (UT) (n = 13). The impact of these methods on inquiries, screening, and enrollment were evaluated by comparing actual enrollment to projected enrollment.

We trained and employed community engagement coordinators to implement strategies in TR and TLE physical therapy clinics. These included, posting flyers, community events, physician outreach, social media ads, and direct mailing. These methods increased study inquiries, screening and enrollment in the study. Specifically, when compared to projected values there were increases in enrollment for both the TR and the TLE groups, but not the UT group. Of those that passed screening 99% of rural and 32% of urban residents enrolled in the study.

A multi-pronged and individualized community engagement approach can increase enrollment of rural residents in clinical trials. Building strong relationships and partnering with community clinics and local communities is essential to success.

Recent revisions to the US Federal Common Rule governing human studies funded or conducted by the federal government require the provision of a “concise and focused” key information (KI) section in informed consent forms (ICFs). We performed a systematic study to characterize KI sections of ICFs for federally funded trials available on ClinicalTrials.gov.

We downloaded ICFs posted on ClinicalTrials.gov for treatment trials initiated on or after the revised Common Rule effective date. Trial records (n = 102) were assessed by intervention type, study phase, recruitment status, and enrollment size. The ICFs and their KI sections, if present, were characterized by page length, word count, readability, topic, and formatting elements.

Of the 102 trial records, 76 had identifiable KI sections that were, on average, 10% of the total length of full ICF documents. KI readability grade level was not notably different from other sections of ICFs. Most KI sections were distinguished by section headers and included lists but contained few other formatting elements. Most KI sections included a subset of topics consistent with the basic elements of informed consent specified in the Common Rule.

Many of the KI sections in the study sample aligned with practices suggested in the preamble to the revised Common Rule. Further, our results suggest that some KI sections were tailored in study-specific ways. Nevertheless, guidelines on how to write concise and comprehensible KI sections would improve the utility and readability of KI sections.

Asthma exacerbations are frequent causes of pediatric hospital admissions. We sought to develop a time series algorithm to forecast next-day daily asthma hospitalizations.

Daily hospitalizations for asthma were collected at Cincinnati Children’s from January 1, 2016, to December 31, 2023. We evaluated Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Prophet, and Ensemble models to forecast next-day asthma hospitalizations validated on 2023 data, considering varying historical training data lengths. Forecasts were calibrated to identify days exceeding a 5% high-risk threshold of historical totals and considered multiple validation years and years before and during the COVID-19 pandemic.

A total of 5,593 hospital admissions were recorded for asthma. Over 2,922 days, 166 days met the 5% high-risk threshold equating to 6 or more admissions. The Ensemble (Median Absolute Percentage Error (MAPE): 46.7%; Positive Predictive Value (PPV): 0.278; Negative Predictive Value (NPV): 0.942; Area Under the ROC Curve (AUC): 0.740; Sensitivity: 0.800; Specificity: 0.656) model achieved higher accuracy of high-risk days than ARIMA (MAPE: 46.5%; PPV: 0.278; NPV: 0.942; AUC: 0.709; Sensitivity: 0.760; Specificity: 0.571), ETS (MAPE: 47.2%; PPV: 0.222; NPV: 0.939; AUC: 0.711; Sensitivity: 0.800; Specificity: 0.668), and Prophet (MAPE: 48.9%; PPV: 0.444; NPV: 0.951; AUC: 0.732; Sensitivity: 0.680; Specificity: 0.741) models.

Our Ensemble model of mean predictions from ARIMA, ETS, and Prophet models was the most accurate in forecasting future asthma hospitalizations. Integrating forecasting techniques with clinical operations could enable proactive prevention through enhanced population care management.

Local nodes on federated research and data networks (FR&DNs) provide enabling infrastructure for collaborative clinical and translational research. Studies in other fields note that infrastructuring, that is, work to identify and negotiate relationships among people, technologies, and organizations, is invisible, unplanned, and undervalued. This may explain the limited literature on nodes in FR&DNs in health care.

A retrospective case study of one PCORnet® node explored 3 questions: (1) how were components of infrastructure assembled; (2) what specific work was required; and (3) what theoretically grounded, pragmatic questions should be considered when infrastructuring a node for sustainability. Artifacts, work efforts, and interviews generated during node development and implementation were reviewed. A sociotechnical lens was applied to the analysis. Validity was established with internal and external partners.

Resources, services, and expertise needed to establish the node existed within the organization, but were scattered across work units. Aligning, mediating, and institutionalizing for sustainability among network and organizational teams, governance, and priorities consumed more work efforts than deploying technical aspects of the node. A theoretically based set of questions relevant to infrastructuring a node was developed and organized within a framework of infrastructuring emphasizing enacting technology, organizing work, and institutionalizing; validity was established with internal and external partners.

FR&DNs are expanding; we provide a sociotechnical perspective on infrastructuring a node. Future research should evaluate the applicability of the framework and questions to other node and network configurations, and more broadly the infrastructuring required to enable and support federated clinical and translational science.

Operationalizing multi-site Community Engagement (CE) Studios to inform a research program is valuable for researchers. We describe the process and outcomes of hosting three CE Studios with Community Experts aged 65 years or older with chronic conditions and care partners of older adults. Experts gave feedback about processes for testing the feasibility, efficacy, effectiveness, and implementation of audio recording clinic visits and sharing recordings with patients who have multimorbidity and their care partners.

The CE Cores of the Clinical and Translational Science Awards Programs at three academic health science centers created a joint CE Studio guide. Studios were conducted iteratively by site. Following receipt of the final reports, responses were compared to find themes, similarities, and differences on four topics in addition to overall commentary: Recruitment and Retention, Study Protocol, Study Reminders and Frequency, and Recording Technology.

Eighteen older adults and care partners in three states provided valuable feedback to inform multi-site trials. Feedback influenced multiple aspects of trials in process or subsequently funded. Experts provided critique on the wording of study invitations, information sheets, and reminders to engage in study procedures. Experts were concerned for participants being disappointed by randomization to a control arm and advised how investigators should prepare to address that.

Multi-site CE Studios should be consecutive, so each team can learn from the previous teams. Using the CES Toolkit ensures that final reports were easily comparable and utilized to develop a research program that now includes three federally funded clinical trials.