Evaluating situational judgment test use and diversity in admissions at a southern US medical school

Introduction Situational judgment tests have been adopted by medical schools to assess decision-making and ethical characteristics of applicants. These tests are hypothesized to positively affect diversity in admissions by serving as a noncognitive metric of evaluation. The purpose of this study was to evaluate the performance of the Computer-based Assessment for Sampling Personal Characteristics (CASPer) scores in relation to admissions interview evaluations. Methods This was a cohort study of applicants interviewing at a public school of medicine in the southeastern United States in 2018 and 2019. Applicants took the CASPer test prior to their interview day. In-person interviews consisted of a traditional interview and multiple-mini-interview (MMI) stations. Between subjects, analyses were used to compare scores from traditional interviews, MMIs, and CASPer across race, ethnicity, and gender. Results 1,237 applicants were interviewed (2018: n = 608; 2019: n = 629). Fifty-seven percent identified as female. Self-identified race/ethnicity included 758 White, 118 Black or African-American, 296 Asian, 20 Native American or Alaskan Native, 1 Native Hawaiian or Other Pacific Islander, and 44 No response; 87 applicants identified as Hispanic. Black or African-American, Native American or Alaskan Native, and Hispanic applicants had significantly lower CASPer scores than other applicants. Statistically significant differences in CASPer percentiles were identified for gender and race; however, between subjects, comparisons were not significant. Conclusions The CASPer test showed disparate scores across racial and ethnic groups in this cohort study and may not contribute to minimizing bias in medical school admissions

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine