This study compared the effects of 3 different approaches to handling missing data (listwise deletion of participants with missing data, mean imputation, and full information maximum likelihood [FIML]) when predicting functional independence trajectories over 10 years in older adults after traumatic brain injury (TBI). Twenty-three TBI Model Systems (TBIMS) inpatient rehabilitation facilities in the United States. Adults who sustained a complicated mild, moderate, or severe TBI at age 60 years or older and needed inpatient rehabilitation. They had to meet all eligibility criteria and have one or more functional independence measure (FIM) scores at 1, 2, 5, or 10 years post-TBI from the TBIMS national database. Retrospective analysis of observational data using hierarchical linear models. FIM total scores at 1, 2, 5, and 10 years post-TBI. Different missing data approaches led to drastically different findings. Model comparisons supported a quadratic effect of time only in the listwise deletion model and found no other significant predictors. Linear trajectories were found in the mean imputation and FIML models. For both these models, older age, underrepresented minority status, unemployment at injury, longer posttraumatic amnesia duration, and pre-injury limitations all predicted lower overall FIM trajectories. However, when compared with the mean imputation model, the FIML-estimated b-weights were larger with smaller P-values. Years of education significantly predicted higher overall FIM trajectories in the mean imputation model but not the FIML model, likely because of the artificial shrinking of the estimated b-weight standard errors in mean imputation. History of mental health treatment predicted lower FIM trajectories only in the FIML model. These findings show that it is critical to use appropriate modern methods to handle missing data because the method can affect outcome trajectory shape and identification of relevant predictor variables. Using older methods for handling missing data, such as listwise deletion, greatly reduces predictive ability, resulting in less generalizability and imprecision in longitudinal rehabilitation research.

ABSTRACT This study examines the effects of affirmative action bans on underrepresented minority (URM) students in U.S. higher education. Using state-level variation and a difference-in-differences approach, it estimates the impacts on enrollment, degree completion, and STEM participation. The results show that these bans reduced Black enrollment and bachelor’s degree attainment at four-year institutions, with the largest declines occurring at highly selective institutions. Black representation in STEM fields decreased at highly selective institutions but increased at moderately selective ones. These findings are consistent with the mismatch hypothesis and suggest that institutional selectivity plays an important role in shaping access, persistence, and academic trajectories.

Current risk prediction guidelines for hypertrophic cardiomyopathy predict only sudden cardiac death and are imperfect, leading to avoidable deaths and unnecessary implantable cardioverter defibrillators. To combine prospectively collected clinical history, imaging, genetic, and biomarker data to improve risk prediction of adverse events in hypertrophic cardiomyopathy. A total of 2750 patients with hypertrophic cardiomyopathy were prospectively enrolled in the registry-based study from 44 sites in North America and Europe with expertise in hypertrophic cardiomyopathy and cardiac magnetic resonance (CMR) imaging. Participants were enrolled from April 1, 2014, to April 7, 2017. Patients underwent a health history questionnaire, blood sampling for biomarkers and genotyping, and contrast-enhanced CMR. Patients were followed up yearly by telephone and through records review regarding event documentation. The predefined composite adjudicated primary end point was time to first event for hypertrophic cardiomyopathy-related deaths; nonfatal sustained ventricular arrhythmias (VAs) requiring cardioversion or defibrillation; and left ventricular (LV) assist device implant or heart transplant. A secondary end point was a composite of sudden cardiac death and nonfatal VA events. The elastic-net method identified the most important predictors. Cox proportional hazards regression assessed associations with time to the first end point. Of the 2750 prospectively enrolled patients, 2698 (98%) had analyzable data after 9 were excluded because they had hypertrophic cardiomyopathy phenocopies and 43 withdrew. Of these remaining patients, 1919 (71%) were male, mean age was 50 years (SD, 11 years), and 423 (16%) were from underrepresented racial and minority groups. The mean follow-up was 6.9 years (SD, 2.1 years). The primary event model in 104 patients included LV scar as a percentage of LV mass by late gadolinium enhancement (LGE%; hazard ratio [HR], 1.86; 95% CI, 1.58-2.20; P < .001), LV mass index (HR, 1.09; 95% CI, 1.01-1.17; P = .03), LV end-systolic volume index (HR, 1.28; 95% CI, 1.12-1.46; P < .001 ), all per 10-unit increase, history of heart failure at study entry (HR, 2.89; 95% CI, 1.75-4.77; P < .001), and log N-terminal pro-B-type natriuretic peptide (NT-proBNP; HR, 1.41; 95% CI, 1.17-1.70; P < .001) level per log unit, (C index for all, 0.77). An LGE percentage of the LV mass of 9% or higher substantially increased the primary composite event rate (P = .001). The secondary sudden cardiac death and VA risk factor model (in 69 patients) included LGE%, LV mass index, LV ejection fraction, and log(NT-proBNP) (C index, 0.76). These results provide prospective evidence for incorporating cardiac magnetic resonance and NT-proBNP in the evaluation of patients with hypertrophic cardiomyopathy. ClinicalTrials.gov Identifier: NCT01915615.

Academic promotion in radiation oncology is influenced by multiple factors, including scholarly productivity and demographic characteristics. While citation-based metrics such as the h-index are increasingly used as objective measures of academic output, the impact of demographic factors such as sex and underrepresented minority (URM) status remains inadequately defined. This study represents the first evaluation of the predictive value of h-index, sex, and URM status on academic promotion. A retrospective cohort of 554 radiation oncologists from 51 NCI-designated Comprehensive Cancer Centers, initially identified in 2019 (T1) and re-evaluated in 2023 (T2), was assessed. Academic promotion status, h-index (2019), sex, URM status, and institutional affiliation were recorded. A generalized linear mixed model assessed associations between these variables and promotion status, with significance defined as P <0.05. The cohort included 203 women (36.7%) and 21 URMs (3.8%); overall, 338 (61%) received promotions between T1 and T2. The mean h-index was 12.3 (median=9), with promoted individuals averaging 15.3 versus 10 for those not promoted. A statistically significant association was found between a higher h-index and promotion ( P <0.0001). Further analysis revealed that neither female sex (odds ratio: 1.02, 95% CI: 0.68-1.52; P =0.94) nor URM status (odds ratio: 0.57, 95% CI: 0.19-1.71; P =0.32) was significantly associated with promotion. In the first examination of the impact of h-index on radiation oncology promotion, a higher h-index is a statistically significant predictor of academic promotion among radiation oncologists. Given limited statistical power to detect differences by demographic characteristics and ongoing underrepresentation of certain groups compared with the population, ongoing work to ensure fair access to opportunities for all remains important.

Radiology Expo Day: Developing a Framework for Increasing Interest, Awareness, and Understanding of Radiology Among Medical Students.

Patient Experience, Gender, Age, Race, and Social Determinants of Health.

The underrepresentation of racial and ethnic minorities in clinical research limits the generalizability of findings. This study examined how specific study design elements, such as follow-up requirements, stipends, and research procedures, were associated with participation refusal rates across racial and ethnic groups in a pediatric emergency department setting. This retrospective cohort study analyzed recruitment outcomes from prospective studies conducted between 2012 and 2022 in a pediatric emergency department with approximately 70,000 annual visits. Studies requiring informed consent were included, excluding those targeting specific racial or ethnic groups. Demographic data were abstracted from the electronic health record using standard categories obtained by self-report during ED registration. Logistic regression assessed associations between study design elements and consent likelihood across racial and ethnic subgroups. Among 14,500 encounters, the median age was 12 years; 14.0% identified as Hispanic, 41.6% as Black, 55.7% as White, and 2.0% as Asian. Recruitment was successful in 71.5% of cases, with a 25.4% refusal rate. Moderate enrollment procedure time (6 to 15min) were associated with higher consent odds for all groups, while very short (<6min) or prolonged durations (>30min) were linked to lower consent odds. High stipends (>$135) were associated with reduced likelihood of consent across all groups. Requirements for biobanking and lab work were more likely to be linked with refusal among Black and Asian participants. Hispanic participants were more likely to consent to medication trials (OR: 1.74, P =0.003). Follow-up requirements were associated with lower consent odds among Hispanic and White participants, with a stronger association for Hispanic participants (OR: 0.39, P =0.002). Study design elements were significantly associated with recruitment outcomes, with notable variation across racial and ethnic groups. Transparent communication, culturally responsive engagement, and logistical flexibility may help reduce participation barriers and promote equity in pediatric clinical research.

Racebending aims at promoting diversity and representation of ethnic minorities in films. It involves casting a Black person for a role that was previously played by a White character, often in the context of a live remake of an existing movie. This approach has generated a range of perceptions among audiences, leading to controversies, particularly online, where people express their views on the matter. While some people favour Racebending, especially Black people who are generally underrepresented in films, others oppose it. This study employed a mixed methods approach, combining sentimental analysis and quantitative research (N=154 respondents), with a focus on the new live remake of Ariel: The Little Mermaid (2023). 2,800 comments have been analyzed. The findings of this study reveal that people generally perceive Racebending negatively. As such, the film industry should consider creating new films for underrepresented minorities rather than relying on live remakes of existing movies. The social media content analysis identified seven categories of people's perceptions, including loss of childhood memories, falsification of the original, inequality, need for new storylines, tokenism, equality, and excitement/enjoyment. Furthermore, the study found that psychological involvement has a significant positive impact on people's perception of racebending. On the other hand, film nostalgia has a significant negative impact on people's perception of racebending. Those who possess limited recollection of film contents and backdrops, and are not inclined to emulate the protagonist's conduct, tend to view racebending in a less unfavourable light.

Predominately White colleges and universities recruit and enroll underrepresented ethnic minority students to help meet the health care needs of the nation. To be successful in the retention of ethnic minority students, predominately White institutions (PWIs) must employ innovative, targeted retention strategies designed to promote successful outcomes in undergraduate nursing programs. A descriptive research design, 14-item survey examining perceptions of effective retention strategies was sent to ethnic minority nurses who attended an undergraduate baccalaureate nursing program at a PWI. Retention strategies that included faculty availability were highly effective for student success among ethnic minority nursing students. Financial aid was found to be especially important to younger students. Nursing programs must integrate retention strategies that ensure faculty are both academically supportive and consistently available to students.

The field of neurosurgery in the Caribbean has long been shaped by systemic limitations in infrastructure, training opportunities, and gender representation. Despite these challenges, a small group of pioneering women have broken through traditional barriers to become leaders in Caribbean neurosurgery. This narrative review highlights the contributions and career trajectories of female neurosurgeons across the region, offering historical and contemporary perspectives on their clinical, academic, and leadership roles. Through a combination of literature review and direct outreach, we document the stories of the first female neurosurgeons in Curaçao, Trinidad and Tobago, Jamaica, Guyana, Saint Lucia, the Dominican Republic, Cuba, and Puerto Rico. Their achievements span advanced surgical innovations, including the introduction of percutaneous transforaminal endoscopic discectomy in the Caribbean, establishment of local neurosurgical departments, and leadership in academic and global neurosurgery initiatives. The article also examines persistent disparities in neurosurgical training and representation, particularly among women and underrepresented minorities. These stories underscore the critical importance of resilience, mentorship, and advocacy in driving progress in a traditionally male-dominated field. By documenting these contributions, we aim to amplify the visibility of Caribbean female neurosurgeons and inspire further efforts to promote equity and diversity in neurosurgery, both regionally and globally.

This is an accepted article with a DOI pre-assigned that is not yet published.This study explores Houston-LSAMP's efforts in retaining and supporting underrepresented minority (URM) students in STEM fields. Established by NSF in 1998, H-LSAMP is a regional alliance of five universities that provides retention programs aimed at increasing URM student graduation rates. However, many of these efforts have now been halted or severely underfunded during the second Trump administration. Recognizing the vital role of such efforts and the ongoing need to support URM STEM students, this study aims to assist existing programs that now operate with little or no funding, or with significantly reduced budgets, by analyzing key components of H-LSAMP that effectively promote student success. This study reveals that while H-LSAMP partner institutions vary in their programmatic approaches, all support minority students through academic development, financial aid, community-building, and professional development opportunities, among other initiatives. Student financial support emerges as the most vital component in promoting success, as it removes financial burdens and enables students to focus on their academics, which increases retention. Building on these insights and the shrinking availability of financial support, this paper suggests several cost-effective strategies to improve URM student success in STEM amidst financial challenges.

ABSTRACT The educational experiences of more than 20 ethnic subgroups within the Asian American category are overlooked when aggregated data reinforces racist tropes that Asians are overrepresented in STEM fields. Using a participatory action research approach, this study argues that HMoob students are pushed out of STEM disciplines in similar ways to other underrepresented minority populations. Of the 66 interviews with current or former HMoob undergraduate students, 28 experienced STEM pushout. We demonstrate that issues of equity in STEM are part of a meritocratic culture that privileges certain forms of knowledge and learning that invisibilize HMoob students through the model minority stereotype. Amplified and institutionalized by the expansion of neoliberal governmentality in US higher education, the prevalence of STEM meritocratic ideologies operationalizes rewards and punishments that drive the competitive culture of STEM through standardized testing, selective enrollment processes, and transactional advising.

ABSTRACT Within the framework of control‐value theory (CVT), this study employed latent profile analysis to identify distinct academic emotion profiles (encompassing enjoyment, anxiety, boredom and foreign language peace of mind) among 1088 secondary school ethnic minority English‐as‐a‐foreign‐language (EFL) learners in China. Four emotion groups were identified: negative emotions, moderate‐level mixed emotions, high‐level mixed emotions and positive emotions. Wald chi‐square tests revealed significant differences in engagement across the four groups in behavioural and cognitive engagement, with the positive emotion group scoring the highest, followed sequentially by the high‐level mixed emotion, moderate‐level mixed emotion and negative emotion groups. In emotional engagement, the positive emotion group still ranked highest and the negative emotion group lowest, while no significant difference was observed between the two mixed emotion groups. Multinomial logistic regression analysis indicated that perceived classroom affordance significantly predicted emotion group memberships. Higher affordance perception increased the likelihood of belonging to the positive emotion group while reducing the probability of classification into the high‐level mixed, moderate‐level mixed and negative emotion groups in sequence. These findings extend the CVT by illuminating the complex emotional experiences of underrepresented ethnic minority EFL learners in China and their correlates with environmental and behavioural factors.

The availability of mentorship for underrepresented minorities in surgery and surgical subspecialties: A systematic review.

Acral lentiginous melanoma (ALM) is a rare subtype of melanoma skin cancer that disproportionately affects underrepresented minorities and is associated with poor prognosis. We report a case of a middle-aged Hispanic woman presenting to the emergency department with an enlarging, painful axillary mass. The initial differential diagnosis focused on occult breast carcinoma, but comprehensive workup with breast imaging was negative. Careful physical examination revealed an undiagnosed pigmented lesion of the left thumb. Excisional biopsy of the left nail unit demonstrated malignant melanoma. Ultrasound-guided core biopsy of left axillary mass confirmed metastatic malignant melanoma. The patient received neoadjuvant immunotherapy followed by left thumb amputation and axillary lymph node dissection. This case illustrates a clinical scenario where melanoma was an unexpected diagnosis that was not in the initial differential diagnoses, underscoring the importance of comprehensive physical examination of non-sun-exposed extremities to facilitate timely diagnosis and management.

The success of hematopoietic stem cell transplantation (HSCT) depends critically on human leukocyte antigen (HLA) matching between donor and recipient. While traditional matching focuses on five classical HLA loci (A, B, C, DRB1, DQB1), clinical practice increasingly considers extended typing at nine loci, including DPA1, DQA1, DPB1, and DRB3/4/5. Furthermore, emerging evidence supports transplantation with up to three HLA mismatches under post-transplant cyclophosphamide (PTCy) regimens. However, current donor search algorithms cannot efficiently identify donors with multiple mismatches across extended HLA loci in real-time. We developed GRIMM-II (GRaph IMputation and Matching, version II), which comprises two novel algorithms: ML-GRIM (Multi-Locus GRIM) for HLA imputation across multiple loci, and ML-GRMA (Multi-Locus GRMA) for real-time donor-patient matching with up to three mismatches. Both algorithms employ a two-stage approach that combines efficient candidate reduction through graph-theoretic frameworks with detailed genotype comparison. ML-GRIM partitions genotypes into class I (HLA-A, B, C) and class II (remaining loci) components, enabling memory-efficient storage and rapid candidate identification. ML-GRMA searches a pre-imputed donor graph composed of donor genotypes and their sub-components, then computes asymmetric graft-versus-host (GvH) and host-versus-graft (HvG) mismatch probabilities to provide clinically relevant compatibility assessments. Both imputation and matching tools are available as a web application at https://grimmard.math.biu.ac.il/ and through GitHub repositories at https://github.com/nmdp-bioinformatics/py-graph-imputation (imputation) and https://github.com/nmdp-bioinformatics/py-graph-match (matching). We validated ML-GRMA and ML-GRIM using the WMDA3 (World Marrow Donor Association) validation dataset, successfully reproducing all previously reported matches while identifying numerous additional candidate donors not detected by previous algorithms. Further validation of ML-GRMA using 3,000 patients with artificially introduced mismatches (0-3 allele substitutions) demonstrated 100% sensitivity and specificity in identifying matching donors at expected mismatch levels. We validated ML-GRIM using simulated nine-locus typings derived from 8,078,224 US donors in the NMDP registry. The algorithm successfully imputed genotypes across variable numbers of typed loci while incorporating multi-ethnic haplotype frequencies. The algorithm achieved real-time performance with typical imputation times under one second and matching times of 1-13 seconds per patient for up to three mismatches, even when searching databases exceeding 8 million donors. Notably, ML-GRMA identified substantially more potentially suitable donors than traditional algorithms by accounting for the biological reality that GvH and HvG mismatches often differ, particularly for donors homozygous at specific loci. To evaluate ML-GRIM performance with low-resolution typing, we tested it on simulated 3-locus typings from the same population. The resulting imputation accuracy correlated with the mutual information between typed loci and complete genotypes. GRIMM-II provides a scalable, memory-efficient solution for nine-locus HLA imputation and real-time identification of donors with up to three mismatches. The graph-based framework supports dynamic registry updates and can readily accommodate additional HLA loci and matching criteria as clinical knowledge evolves. By expanding the pool of acceptable donors while maintaining computational efficiency, GRIMM-II addresses a critical need in contemporary transplantation practice, particularly for patients from underrepresented ethnic minorities who face lower probabilities of finding perfectly matched donors.

The undergraduate political science program at UCR serves a higher proportion of students who are underrepresented minorities, first-generation, or Pell Grant recipients. Employment outcomes for this group could be better, but the institution itself is not geared for such an intervention. The Department of Political Science introduced its own career intervention initially as a series of career workshops and then as a single-credit course. The course format was helpful in some ways but still fell short of faculty expectations.

The experiences of underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) programs provide valuable insights into improving their retention and academic achievement. This study investigates how Black and Hispanic calculus students describe faculty-student interactions. Four students participated in a demographic survey and follow-up interviews, sharing their experiences with calculus learning. Using thematic coding guided by validation theory, the data revealed instructors’ academic practices that either validated or invalidated students’ participation in calculus classes. These practices include cultivating in-class conceptual support, promoting collaborative learning spaces, demonstrating out-of-class academic availability, acknowledging and integrating diverse student experiences, and encouraging metacognitive reflection. The findings amplify student voices and offer recommendations for calculus instructors and institutions to enhance persistence, sense of belonging, and academic success among Black and Hispanic students.

A sense of belonging, engagement, self-confidence, and adequate college preparation are known predictors of student success, particularly in undergraduate retention and graduation. Yet, bottleneck courses often hinder academic progress. The use of a virtual peer-led study application has shown promise in supporting student achievement through collaborative learning. This pilot study examined whether participation in a virtual peer-led study application reduced DFW (grades of D, F, or course withdrawal) rates in a bottleneck STEM (Sciences, Technology, Mathematics) course, Organic Chemistry. Logistic regression and analysis of variance (ANOVA) results indicated that participants using a virtual peer-led study application had 27% lower odds of receiving a DFW grade than non-participants. Prerequisite Chemistry GPA strongly predicted course outcomes: each one-point increase corresponded to a 67.5% reduction in DFW odds. Conversely, underrepresented minority (URM) and Pell Grant recipient statuses were linked to higher DFW odds—76% and 67% higher, respectively. Findings suggest that peer-led, technology-enhanced learning can strengthen academic performance and foster meaningful peer connections. The results also demonstrate how learning analytics can illuminate students’ capacity to absorb, process, and retain knowledge, reinforcing the theoretical foundation of collaborative learning in challenging STEM courses. Considering the contributions of virtual peer-led learning to improving students’ academic performance, particularly in STEM courses, this study’s most important finding is the confirmation that prior Chemistry GPA plays a fundamental role in predicting students’ DFW outcomes in Organic Chemistry.

This study presents a scoping review of dementia risk among ethnoracial minorities in machine learning (ML)-based studies. While ML methods are widely used in dementia research, their benefits for ethnoracial minorities remain unclear. We conducted a systematic search using keywords related to ML, dementia, and ethnoracial minorities in major electronic databases. Of the 599 initially identified articles, 14 met the inclusion criteria after screening. Most studies relied on existing datasets, with ethnoracial minority representation ranging from 5 to 75% of the sample. Most studies categorized race as a predictor, aggregating it into broad groups. Among studies using racial minorities as a predictor, results consistently showed a lower dementia risk among non-Hispanic Whites compared to Hispanics and non-Hispanic Blacks. Various ML techniques were employed. Our review highlights the persistent underrepresentation of ethnoracial minorities in national datasets used in ML studies, limiting the understanding of racial disparities in dementia risk. By synthesizing existing ML-based dementia studies, this review identifies key methodological and data-related gaps in how ethnoracial minority populations are represented and analyzed. With the growing ethnoracial diversity, ML-based studies must prioritize representative datasets to accurately capture dementia risk across groups.