Many Paths to the Summit: Survey of Step 1 Study Methods with Pass/Fail Scoring.

Introduction: After the United States Medical Licensing Examination (USMLE) shifted the Step 1 licensure exam to pass/fail, there have been limited studies to analyze changes in student study strategies. Material and Methods: Surveys were distributed to third- and fourth-year medical students at the University of Michigan Medical School (UMMS). The response rate was 66%. Results: The largest proportion (18.81%) of students chose 8 weeks of study time. 40.59% of students increased the length of their study period. To determine time allocated for dedicated study, 37.26% of respondents consulted near-peers who had already taken Step 1. Students also considered prior experiences with standardized tests (15.57%), personal reasons (14.62%), and conversations with school advisors (13.21%). 44.55% of students studied for 9-11 h a day, and 42.57% studied for 5-8 h a day. 52.69% of students scored between 70 and 80% on their final practice NBME test before their Step 1 exam. One hundred percent of respondents passed the exam. Exam non-extenders achieved higher end average practice test scores with shorter study periods. No differences in Step 1 study time or intensity were found when comparing students by intended specialty competitiveness. Conclusion: Our results demonstrated patterns in study strategies for the new pass/fail Step 1 exam that may prove useful for curriculum design and schedule plan for future cohorts. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-024-02072-2.

Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis.

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Automated Spatial Omics Landscape Analysis Approach Reveals Novel Tissue Architectures in Ulcerative Colitis.

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Certified Nurse-Midwives as Teachers: Expanding Interprofessional Collaboration Learning Opportunities for Medical Students on the Obstetrics and Gynecology Clerkship.

INTRODUCTION: Interprofessional experiences provide critical exposure to collaborative health care teams, yet medical students often lack this experience during clinical clerkships. We created a labor and delivery triage rotation exclusively staffed by certified nurse-midwives in the obstetrics and gynecology clerkship to address this gap. We sought to evaluate the clinical learning experiences of medical students rotating on this midwife-supervised collaborative team. METHODS: Between 2018 and 2020, we electronically sent all medical students an evaluation after each required clerkship. Our primary outcome was the quality of teaching score, as defined by level of agreement on a 5-point Likert scale with a statement regarding teaching quality. A secondary outcome evaluated scores from the National Board of Medical Examiners (NBME) Obstetrics and Gynecology subject examination taken by all students at the end of the rotation. Evaluations and assessments were compared between students rotating on labor and delivery triage and those who did not. Evaluation comments from students rotating on labor and delivery triage were collated to identify common themes. RESULTS: Of 374 students, 370 (98.9%) completed the end-of-clerkship course evaluation and 312 (83.4%) completed teaching evaluations. Sixty-seven students (17.9%) rotated in labor and delivery triage; of these, 52 (77.6%) completed both the course and triage rotation evaluations. There were no differences in the mean quality of teaching scores (3.9 ± 1.2 versus 3.8 ± 1.2, p = 0.54) or in NBME Obstetrics and Gynecology subject examination scores between students rotating in labor and delivery triage compared to all other rotations (79.9% ± 7.2% vs 80.2% ± 7.8%, P = 0.436). Comments from teaching evaluations highlighted student exposure to the midwifery philosophy of care model. DISCUSSION: This work demonstrates the feasibility and benefits of this midwife-led authentic interprofessional collaborative experience for medical students. This model can serve as an example of how to implement and evaluate interprofessional collaboration experiences in the clinical setting.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.

Association of a longitudinal, preclinical ultrasound curriculum with medical student performance.

INTRODUCTION: Point-of-care ultrasound (US) is used in clinical practice across many specialties. Ultrasound (US) curricula for medical students are increasingly common. Optimal timing, structure, and effect of ultrasound education during medical school remains poorly understood. This study aims to retrospectively determine the association between participation in a preclinical, longitudinal US curriculum and medical student academic performance. METHODS: All first-year medical students at a medical school in the Midwest region of the United States were offered a voluntary longitudinal US curriculum. Participants were selected by random lottery. The curriculum consisted of five three-hour hands on-sessions with matching asynchronous content covering anatomy and pathologic findings. Content was paired with organ system blocks in the standard first year curriculum at our medical school. Exam scores between the participating and non-participating students were compared to evaluate the objective impact of US education on performance in an existing curriculum. We hypothesized that there would be an association between participation in the curriculum and improved medical student performance. Secondary outcomes included shelf exam scores for the surgery, internal medicine, neurology clerkships and USMLE Step 1. A multivariable linear regression model was used to evaluate the association of US curriculum participation with student performance. Scores were adjusted for age, gender, MCAT percentile, and science or engineering degree. RESULTS: 76 of 178 students applied to participate in the curriculum, of which 51 were accepted. US curriculum students were compared to non-participating students (n = 127) from the same class. The US curriculum students performed better in cardiovascular anatomy (mean score 92.1 vs. 88.7, p = 0.048 after adjustment for multiple comparisons). There were no significant differences in cumulative cardiovascular exam scores, or in anatomy and cumulative exam scores for the gastroenterology and neurology blocks. The effect of US curriculum participation on cardiovascular anatomy scores was estimated to be an improvement of 3.48 points (95% CI 0.78-6.18). No significant differences were observed for USMLE Step 1 or clerkship shelf exams. There were no significant differences in either preclinical, clerkship or Step 1 score for the 25 students who applied and were not accepted and the 102 who did not apply. CONCLUSIONS: Participation in a preclinical longitudinal US curriculum was associated with improved exam performance in cardiovascular anatomy but not examination of other cardiovascular system concepts. Neither anatomy or comprehensive exam scores for neurology and gastrointestinal organ system blocks were improved.

Concealment of a Sexual Minority Identity in the Workplace: The Role of Workplace Climate and Identity Centrality.

Concealing a sexual minority identity at work has been linked to adverse health and employment outcomes. The authors of this paper examine whether LGBQ employee levels of concealment from coworkers and supervisors are linked to perceived workplace climate and LGBQ-identity centrality. Rainbow Illinois 2010-2011 data were used; N = 319 LGBQ full or part-time employees. Multiple regression with moderation was used to test the hypotheses. Support and hostility in the workplace each had a negative main effect on concealment from coworkers as well as supervisors. The interaction between support and centrality was also significant. LGBQ employees who placed greater importance on their sexual orientation were more likely to disclose that identity to supervisors in the face of hostility than those with less central identities. Thus, sexual minorities may be more responsive to workplace climate when they have more central identities.

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios-users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.

COVID-19 and medical education in Africa: a cross sectional analysis of the impact on medical students.

BACKGROUND: The African continent currently experiences 25% of the global burden of disease with only 1.3% of the world's healthcare workers. The COVID-19 pandemic has caused unprecedented disruption to medical education systems, increasing the strain on already-vulnerable regions. Our study examines the impact of COVID-19 on medical students across 33 countries in the African continent. METHODS: A 39-item anonymous electronic survey was developed and distributed to medical students across Africa through social networks to assess the impact of the COVID-19 pandemic on medical education. The survey assessed the domains of: class structure changes and timing, patient interactions, exam administration, learning environment satisfaction, mental health impacts, and volunteer opportunities/engagement. RESULTS: 694 students across 33 countries participated. 80% of respondents had their classes suspended for varied lengths of time during the pandemic, and from these students 59% of them resumed their classes. 83% of students felt they were in a supportive learning environment before the pandemic, which dropped to 32% since the start. The proportion of students taking exams online increased (6-26%, p<0.001) and there was a decrease in the proportion of students seeing patients as a part of their education (72-19%, p<0.001). CONCLUSIONS: COVID-19 is harming medical students in Africa and is likely to worsen the shortage of the future's healthcare workforce in the region. Pandemic-related impacts have led to a degradation of the learning environment of medical students. Medical schools have shifted online to differing degrees and direct patient-care in training of students has decreased. This study highlights the urgent need for flexible and innovative approaches to medical education in Africa.

Autonomous adaptive data acquisition for scanning hyperspectral imaging.

Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.