Journal of Clinical and Translational Science最新文献

Introduction: Historically US-based academic organizations dedicated limited resources, including policies, personnel to ensuring compliance with clinical trials registration and results reporting requirements. A recent follow-up survey finds that 6-years after an initial survey, there is increased attention and dedication of resources to improve compliance rates for clinical trials registration and results reporting.

Methods: Internet-based online survey using Qualtrics between 20 April 2023 and 30 September 2023 distributed to Protocol Registration and Results Reporting (PRS) Administrators at US-based academic organizations with ClinicalTrials.gov organizational accounts. The survey focused on the 249 respondents of the original 2016-2017 survey published in 2018. The overall response rate was 162/249 (65.06%) with 100% participation from National Cancer Center (NCI) Designated Cancer Centers and hubs of the Clinical and Translational Science Awards (CTSA).

Results: Results indicated a marked increase of academic organizations with policies in place for registration (43 to 74%) and results reporting (35 to 68%). The median number of Full-time Equivalent (FTE) staff at responding academic organizations increased (from 0.08 to 0.5) with statistically significant difference between the number of organizational records and FTEs supporting registration and results reporting. Larger gains are seen with NCI-Designated Cancer Centers and/or CTSA hubs.

Conclusions: It appears academic organizations are more equipped to comply with requirements, and demonstrate a trend towards appropriate staffing. In the 6 years since the original survey, US-based academic organizations have significantly increased attention to compliance with clinical trials registration and results reporting requirements, indicated by an increase in institutional policies and dedicated personnel.

Clinical data science, like the broader discipline of all data science, has quickly grown from obscurity only a few decades ago to one of the fastest growing specialties in biomedical research today. Yet, the education and training of the workforce has not kept pace with the growth of the field, the complexity of science, or the needs of the profession. The purpose of this paper is to provide a template for an intensive short course on fundamentals of clinical data science that meets the needs of working professionals in academic, industry, and government research settings. Care will be taken to introduce students to essential roles, responsibilities, and practice patterns within the field, the foundational components from which they come, and many of the soft skills needed for professional practice and advancement in the field today. The course is designed as an evidence-based, immersive learning experience taught over a 5-day period on a university campus, taught using principles of best educational practice and multiple modalities, to assure optimal interaction and engagement throughout the week. This template may be reproduced by any institution interested in and capable of offering such a program.

ASReview is a software that can potentially reduce the workload of literature screening in systematic reviews by ranking the retrieved records. We assessed the tool's feasibility, advantages, and limitations, to populate a database of cancer immunotherapy trials. ASReview is easy to use, and it efficiently identified relevant records. It may save resources compared to traditional systematic reviews using two human reviewers. Predefined procedures are necessary to maintain a transparent and reproducible workflow. Limitations include that adding references to existing projects is difficult and that the algorithm learns from every decision, even when this may not be appropriate.

Cardiovascular disease (CVD) and dementia are leading causes of death in women, with dementia disproportionately affecting females. Both share risk factors such as type 2 and gestational diabetes. While diabetes and CVD risk factors are well studied, gaps remain in understanding dementia's lifespan influences, sex-specific effects, and social determinants. This report advocates a convergence science approach, integrating basic, behavioral, and implementation sciences, to address these gaps. We propose a novel framework to examine shared cardiometabolic risks across the lifespan, enabling targeted early interventions to reduce dementia burden and improve heart-brain health outcomes in women.

Background: The implementation of artificial intelligence (AI) tools into clinical spheres emphasizes the critical need for an AI-competent healthcare workforce that can interpret AI output and identify its limitations. Without comprehensive training, there is a risk of misapplication, mistrust, and underutilization. Workforce skill development events such as workshops and hackathons can increase AI competence and foster interdisciplinary collaboration to promote optimal patient care.

Methods: The University of Florida hosted the AI for Clinical Care (AICC) workshop in April 2024 to address the need for AI-competent healthcare professionals. The hybrid workshop featured a beginner and advanced track with interactive sessions, hands-on skill development, and networking opportunities led by experts. An anonymous, voluntary post-workshop survey asked participants to score their knowledge and skills before and after the AICC workshop. A second, follow-up survey was administered approximately nine months later.

Results: Ninety participants attended the AICC workshop, forty-one attendees completed the post-workshop survey, and six attendees completed the follow-up survey. Paired T-tests of the post-workshop survey revealed statistically significant (P < .001) increases in self-reported knowledge gain across all six beginner track learning objectives and significant (P < .05) increases across all five advanced track objectives. Feedback indicated participants appreciated the interactive format, although communication and networking needed improvement.

Conclusion: The AICC workshop successfully advanced AI literacy among biomedical professionals and promoted collaborative peer networks. Continued efforts are recommended to enhance participant engagement and ensure equitable access to AI education in clinical settings.

Introduction: Endometriosis and chronic pelvic pain (CPP) are complex conditions that significantly impact quality of life. Few tools systematically capture patient-reported outcomes in this population. This pilot study evaluated patients' experiences and the perceived usability of an electronic Patient-Reported Outcome (ePRO) tool to assess its feasibility in supporting a clinical data registry. Associations between demographic/clinical characteristics and ePRO usability were also explored.

Methods: This prospective observational study included patients enrolled at a tertiary endometriosis and CPP clinic who completed a REDCap-based ePRO survey remotely. The survey included demographic items and 13 validated instruments assessing pain, psychological distress, sensory processing, and quality of life. Usability was evaluated through an Online Questionnaire-Experiences Survey (OQES), covering accessibility, completion experience, redundancy, and content relevance. Descriptive statistics, t-tests, and Hedges' g were used for analysis; open-ended responses were thematically reviewed.

Results: Fourteen patients were invited; 11 (78.6%) completed the full ePRO. Most found it easy to access (90.9%) with stable internet (100%). While 63.6% reported some redundancy, none reported discomfort, and 90.9% agreed the survey captured relevant experiences. Participants with higher Central Sensitization Inventory (CSI) and Generalized Anxiety Disorder-7 (GAD-7) scores were more likely to complete all items (P = 0.042 and .047). Those who did not perceive redundancy scored significantly higher on the Pain Catastrophizing Scale (P = .048) and Endometriosis Health Profile-30 (P = .016).

Conclusion: The ePRO tool showed high feasibility. Patients with higher symptom burden were more likely to find it useful. Future improvements should reduce redundancy and clarify survey instructions.

Reference standards are vital for harmonizing heterogeneous clinical data for research, but little is known about their implementations or costs. We surveyed NIH Clinical and Translational Science Awards (CTSA) hubs to assess operational dimensions of institutional implementation, maintenance, and use of the Logical Observation Identifiers, Names, and Codes (LOINC) standard. Respondents (n = 19,30%) exhibited substantial variability in approaches to implementation. Differences in number and training of staff and frequency of mapping updates make it difficult to estimate costs and comparability of data across sites. CTSA and other multi-site research can benefit from operational definitions and recommended processes for LOINC implementation.

Background: Artificial intelligence (AI) technology is rapidly entering biomedical research, and there is a need to assess and develop curricula that address trainees' learning objectives and interests. Studies of biomedical workforce development show that the prospective engagement of students in formulating educational objectives and activities improves motivation and learning outcomes. This study aimed to explore the educational applications of a novel AI-powered technology in undergraduate education.

Methods: A mixed-methods approach using elicitation interviews and cultural domain analysis was applied to identify the salience of ideas around the educational uses of Functional Unit State Identification & Navigation with Whole Slide Images (FUSION), an AI-powered cell-visualization technology. Interviews from 21 students were reduced to learning application statements and assessed for cultural salience and clustering for potential educational applications.

Results: Saturation was reached after 11 interviews, and analysis resulted in eight clusters of 25 unique consensus-based statements. Students thought of the technology as a tool for cell analysis and measuring, but they also viewed applications for medical and K-12 education, public engagement, and note-keeping for technology research. Methodologically, our study demonstrates the potential of cultural consensus for learner-centered curriculum development.

Conclusions: Our findings suggest that trainees perceive many educational uses of FUSION, including those that fit traditional biomedical research curricula and translational applications. Trainees should be engaged in co-design to support and guide technology translation for educational use.