Multicenter clinical trials have become increasingly larger and more complex yet assuring high-quality data remains an essential task for data coordinating centers.

A flexible algorithm is presented to exhaustively search for potential data anomalies by participant and site. The algorithm proceeds as a three-phase collaboration between the data coordinating center and clinical sites. First, participant-level data are examined in a univariate approach for all relevant variables. Values at the extreme tails of the distribution that also lie outside range checks and are previously unverified by clinical sites are deemed potential outliers. Second, participant-level data are examined in a multivariate machine learning approach among meaningful groups of related variables, e.g., weight and body mass index. Third, site-level differences are characterized using statistical tests and standardized differences, both adjusted and unadjusted for site demographics. Findings are discussed with sites and, if needed, alterations can be made to procedures for data collection. For illustration, the algorithm is applied to data collected in the Molecular Transducers of Physical Activity Consortium (MoTrPAC) study.

Application of the algorithm to MoTrPAC yielded an evaluation of over 1.9 million observations in n = 1029 study participants. Numerous individual univariate, multivariate, and site-level outliers were identified that were previously unidentified by existing data evaluation processes.

It is recommended to apply this algorithm to a subset of participants early in a study, with repeated explorations over subsequent intervals throughout the study. The goal is to maximize data quality, particularly critical to the increasing occurrence of open-source, data resources.

Vaccinations play a crucial role in public and personal health. Vaccines such as the Yellow Fever 17D vaccine are effective at preventing disease and provide lifelong immunity with few adverse events. Leveraging the increase in electronic medical records and the widespread use of machine learning algorithms in healthcare, this study aims to investigate and develop an algorithmic framework for analyzing clinical data on Yellow Fever 17D vaccinations in order to predict at-risk populations for adverse events following immunizations. This research incorporates parameters from the patient’s medical history and demographic information.

To build our analytic framework, we tested five machine learning algorithms: random forest, gradient boost, logistic regression, XGBoost, and Bernoulli naïve Bayes. We cleaned and managed the datasets through EHRchitect software. We assessed the performance of the algorithms using standard metrics, including precision, recall, F1-scores, accuracy, and the area under the receiver operating characteristic curve. Additionally, we implemented population sampling to mitigate potential biases common in clinical data and performed parameter ranking analysis to identify the most influential features in classifying patient reaction outcomes.

We found that logistic regression produced the highest performance, and through hyperparameter optimization, we developed a framework with precision, accuracy, and ROC scores of 87.7%, 99.0%, and 0.818, respectively.

In conclusion, this study examined five modeling algorithms to establish a framework for analyzing real-world data and to predict at-risk populations for adverse events following immunization. We incorporated a logistic regression algorithm to assess our clinical data to achieve a high-precision performance model for classifying patient predictions.

Federally Qualified Health Centers (FQHCs) serve diverse populations and may enhance participant diversity in clinical trials; however, their engagement in trials remains understudied. The objective of the current study was to examine participant diversity in hypertension (HTN) and type-2 diabetes (T2D) clinical trials.

We searched PubMed, Cochrane, CINAHL (Cumulative Index of Nursing and Allied Health Literature), Web of Science, Embase, and Scopus for clinical trials of medical interventions addressing HTN or T2D among adults (≥18), engaging one or multiple FQHCs, and published between 2013 and 2023. We reported pooled proportions of representation of underserved populations in clinical trials.

Of 4552 articles identified, 26 clinical trials were included. The pooled proportion was 0.35 (95% CI: 0.26, 0.45) for Hispanic, 0.36 (95% CI: 0.23, 0.48) for Black/African American, 0.03 (95% CI: 0.02, 0.05) for Asian, 0.58 (95% CI: 0.54, 0.63) for female, and 0.25 (95% CI: 0.07, 0.43) for uninsured participants, and 0.54 (95% CI: 0.36, 0.72) for people with limited education (≤high school).

Trials involving FQHCs demonstrated high inclusion of historically underrepresented than typical trial populations. This underscores the potential of FQHCs to improve clinical trial representativeness and to enhance the generalizability of research findings.

Research participants express strong desires to receive their individual research results (IRR), consistent with key ethical principles of research. Participants’ preferences, especially when based on past experiences with IRR, can help in formulating approaches to the process.

Participants (n = 149) were recruited from a site of the eMERGE-IV study, which returned risk estimates for 8 common diseases based on polygenic scores and other factors. Most were White, highly educated, and English-speaking. Participants completed an online survey regarding their eMERGE experiences, the impact of IRR on future research participation, and their preferences for returning results.

Respondents indicated that receiving results would be very important in their decisions about future studies; 64.9% gave it the highest importance rating. They wanted IRR to improve their own and their families’ health. Concerns about risks were low. Most said they were likely to share the information with other people. Respondents preferred receiving results from health professionals associated with the study, as opposed to their own physicians. They favored return by email and videoconferencing, as opposed to in-person visits or mail. Respondents desired information in various formats to help them understand and act on the results.

These results underscore the importance that research participants place on IRR and provide guidance on the best ways to provide them. A strength of these findings is that they reflect the experiences of respondents who already received IRR. Investigators should consider how best to offer IRR to their participants; funders should provide appropriate support for the process.

In the COVID-19 pandemic, several phase II and III randomized trials were launched to evaluate the effectiveness of camostat, an orally administered TMPRSS2 inhibitor previously approved for other indications, for treating SARS-CoV-2 infections. Owing to the rapidly changing landscape during the pandemic, many of these trials were unable to reach completion. Further, methods for synthesizing trials that were launched and not completed were critical.

This systematic review aimed to consolidate global evidence by identifying placebo controlled, randomized trials of camostat and analyzing their collective clinical and virologic impact on SARS-CoV-2 through an individual patient data meta-analysis (IPDMA). We harmonized data from the studies and utilized Bayesian statistical models to assess virologic outcomes (measured by the rate of change in viral shedding) and clinical outcomes (based on the time to the first of two consecutive symptom-free days), adjusting for age and sex.

The IPDMA incorporated data from six countries, totaling 431 patients across the studies; 118 patients contributed data for the primary virologic outcome and 240 for the clinical symptom outcome. Camostat did not improve the rate of change in viral load (difference in rate of change = 0.11 Ct value/day higher, 95% credible interval 2.04 lower to 2.23 higher) or time to symptom resolution (hazard ratio = 0.87, 95% credible interval 0.51, 1.55) when compared to placebo.

Despite its theoretically promising mode of action, camostat did not demonstrate a statistically significant virologic or clinical benefit in treating COVID-19, highlighting the complexity of drug repurposing in emergency health situations.

Translational researchers often occupy dual roles as clinicians and investigators, creating ethical tensions that are inadequately addressed by traditional training in research ethics or clinical ethics. This study aimed to design, pilot, and evaluate a short course to improve early-stage translational researchers’ self-efficacy with respect to navigating conflicts between their roles as researchers and clinicians or community advocates.

An inter-disciplinary team at University of Rochester developed an 8-module course aimed at improving self-efficacy in moral decision-making. Using Rest’s Four Component Model, the eight course sessions focused on moral sensitivity, reasoning, motivation, and action with respect to dual-role ethical issues such as expanded trial access, ancillary obligations, and return of results. Thirteen early-stage translational researchers participated in a pilot course delivered in person from March through May 2024. Evaluation included pre-and post-course surveys and focus groups.

Participants demonstrated significant improvements in self-efficacy for moral sensitivity (mean increase .79, p < .001), moral reasoning (mean increase .80, p = .001), and ability to take moral action (mean increase .67, p = .002). Participants’ attitudes toward translational research were generally stable. Focus groups revealed that participants highly valued the course, but desired greater curricular cohesion and more guidance on implementation strategies.

This pilot course successfully enhanced early-career translational researchers’ self-efficacy for ethical decision-making in dual-role contexts. The findings suggest that ethics education tailored to translational researchers’ unique challenges can meaningfully improve their capacity to navigate complex ethical tensions.

Translational science (TS) workforce development initiatives are emerging to fast-track conversion of findings into applications to improve health. Principles of TS were developed to guide workforce development; however, few tools are available to evaluate confidence related to these principles to benchmark progress.

Our goal was to develop and validate a scale for assessing confidence related to the seven TS principles and get feedback about future workforce development opportunities that could be evaluated with this scale.

A cross-sectional survey was conducted in 2025 engaging 158 researchers and research staff affiliated with three Clinical and Translational Science Award hubs in Alabama, Massachusetts, and Ohio. Factor analysis was conducted to determine interrelatedness among the TS principles, reliability was assessed using Cronbach’s alpha, and mixed effects models were used to examine differences in confidence levels.

Four factors emerged (α = 0.95 for full scale, 14 items), including Taking Risks for Impact (α = 0.85, 2 items), Multi-perspective Research (α = 0.88, 3 items), Collaborative Methods (α = 0.89, 6 items), and Efficiency and Productive Failure (α = 0.88, 3 items). Confidence levels varied significantly by factor (p < 0.001) with the lowest mean confidence (1 = lowest, 5 = highest) for the Efficiency and Productive Failure (2.56) and Taking Risks for Impact (2.79) factors. There were no differences in mean levels of confidence by demographics, site, or experience.

Findings provide guidance for prioritizing topics for future TS workforce development initiatives as well as a brief scale for evaluating how these interventions cultivate confidence across the seven TS principles.