Physicians face increasing difficulty accessing relevant clinical evidence due to time constraints and the fragmentation of biomedical literature across multiple databases. Existing search platforms often require separate queries and may prioritize a single source, limiting the breadth and efficiency of evidence retrieval. This project aimed to develop and evaluate an artificial intelligence (AI)-based system designed to aggregate and prioritize clinical information from multiple open-access medical databases.
A multi-source retrieval tool was developed that integrates results from PubMed, Semantic Scholar, Directory of Open Access Journals (DOAJ), CORE, the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), PLOS, and the National Network of Libraries of Medicine (NNLM). The system uses AI-assisted query processing and a source-weighting strategy to prioritize databases based on relevance to the query topic. Design decisions emphasized accessibility, ethical use of open-access content, and integration with clinical workflows.
Preliminary testing using representative clinical queries demonstrated the system’s ability to retrieve evidence from multiple complementary sources, increasing evidence diversity compared to single-database searches. Informal physician feedback highlighted the potential value of integrated retrieval for improving search efficiency and supporting evidence-based decision-making.
This work represents an early-stage clinical informatics approach to addressing information overload in healthcare. Future work will include structured usability testing with physicians, refinement of source prioritization algorithms, and evaluation of time savings and clinical relevance. AI-driven evidence aggregation tools may support more efficient clinical decision-making and improve access to high-quality medical information.

According to official AAMC data, 72.7% of entering U.S. medical students in 2025 took a gap year—one or more years between obtaining an undergraduate degree and matriculating into medical school. This represents a 22% increase compared to matriculating students in 2016, less than 10 years prior. Despite this rapidly increasing trend in medical school admissions, little scholarly research exists on how taking a gap year affects admission to medical school. The long-term goals of this study are to 1) identify factors that determine whether a pre-medical student may benefit from a gap year, 2) evaluate how a gap year may strengthen a medical school application, and 3) determine whether a gap year may improve or predict successful matriculation to medical school, questions that are currently not well understood. This project compiles current scholarly literature and data on pre-medical gap years to assess the existing knowledge on this topic. This study conducted a PRISMA systematic review of pre-medical gap year literature, categorizing works based on whether gap years were viewed favorably, neutrally, or negatively and analyzing them within the framework of the AAMC Premed Competencies. The literature review found that themes consistent with the AAMC Premed competency “commitment to learning and growth” were mentioned most frequently in discussions and opinions of gap years. Development of the competencies “interpersonal skills” and “empathy and compassion” during a gap year was most strongly supported by both qualitative and quantitative data. Notably, the review revealed that most available research examines gap years retrospectively, analyzing qualities of current medical students or residents that were influenced by their gap year. However, little research examines undergraduate students prospectively and their decision-making process regarding whether to take a gap year before applying to medical school. These findings highlight a significant gap in pre-medical gap year research that should be addressed in future studies to better guide pre-medical students and their advisors in decisions about taking a gap year and how it may affect admission outcomes.

The Frogs Aiding Dragons College Initiative works with the TCU organization Frogs Aiding Immigrants and Refugees (FAIR) to support Fort Worth immigrant and refugee communities, especially through partnerships with the International Newcomer Academy (INA). INA is a school specifically for 6th-9th grade refugee students. Many of these students have had no educational background or don’t fluently read or speak English. So, the goal of Frogs Aiding Dragons College Initiative is to encourage students to continue pursuing an education and convey that college is a possible goal for them. We work with a group of 62 9th graders where we bring them to TCU and host a Thanksgiving feast, campus tour, and panel with TCU immigrant students. We then bring the college experience to INA with presentations and hands-on activities from various students representing various TCU departments, including Chemistry, Pre-Health, the Fine Arts, and Engineering. We assess the effectiveness of this initiative using a survey measuring INA students’ attitudes towards desire to attend college, how much they know about college, and if they feel like they have more resources to apply to and attend college.

Background and Hypothesis
Intraosseous (IO) access is a critical emergency procedure used when rapid vascular access cannot be obtained through traditional intravenous routes. Despite its life-saving potential, many trainees have limited opportunities to practice IO placement in clinical settings. Simulation-based training offers a safe environment to develop procedural competency before performing the technique on patients. Advances in 3-D printing technology allow for the creation of realistic, cost-effective procedural models that may enhance hands-on training. We hypothesized that a 3-D printed IO training model would provide an effective and engaging method for novice learners to practice IO placement and would improve learner confidence in performing the procedure.

Methods
We conducted a simulation-based educational study using a custom 3-D printed IO training model designed to replicate relevant bony anatomy. Participants consisted of novice learners undergoing procedural skills training. Learners were provided instruction on IO access followed by hands-on practice using the 3-D printed model. Participants performed IO placement using a standard IO drill system. Following the training session, participants completed a survey evaluating their confidence in performing IO access and their perceptions of the model as a training tool. Descriptive analysis was performed to assess learner experience and perceived educational value.

Results
Participants reported that practicing IO placement on the 3-D printed model was an engaging and effective method for learning the procedure. Learners demonstrated the ability to successfully establish IO access using the simulation model. Post-training surveys indicated increased confidence in performing IO placement and positive perceptions of the realism and educational utility of the model.

Conclusion
Simulation training using a 3-D printed IO model provides an accessible and effective approach for teaching IO access to novice learners. Participants reported improved confidence and positive learning experiences after practicing with the model. These findings support the use of 3-D printed simulation models as a valuable tool for procedural education and informed the development of a follow-up study designed to further investigate procedural complications and technique optimization during IO placement.

In quantitative studies comparing a treatment and a control group, treatment effect is often viewed simply as the difference in group means. However, any treatment can have an impact beyond simply shifting the mean outcome. In this work, we consider a linear treatment effect (LTE) model, meaning we simultaneously consider the difference in means and the ratio of standard deviations between two populations to better characterize the effect of the treatment. Estimation is done using an empirical likelihood (EL) formulation. The EL framework provides a nonparametric approach for conducting inference without making strong assumptions about the underlying population model. Generally, the EL statistic has a limiting chi-square distribution. However, in small sample settings, the EL statistic can exhibit strong deviations from this ideal. To address this issue, we investigate the use of the Bartlett correction, which is a multiplicative adjustment to the EL statistic to improve the chi-square approximation. This correction has been shown to substantially improve confidence region coverage accuracy, especially for small and moderate sample sizes. Through simulation, we examine the performance of the EL statistic in the LTE model, with and without a Bartlett correction applied. Our results demonstrate that the Bartlett-corrected EL approach provides improved performance, yielding confidence regions with coverage closer to desired nominal levels.