Trainee advocacy for medical education on the care of people with intellectual and/or developmental disabilities: a sequential mixed methods analysis.

BACKGROUND: Medical trainees (medical students, residents, and fellows) are playing an active role in the development of new curricular initiatives; however, examinations of their advocacy efforts are rarely reported. The purpose of this study was to understand the experiences of trainees advocating for improved medical education on the care of people with intellectual and/or developmental disabilities. METHODS: In 2022-23, the authors conducted an explanatory, sequential, mixed methods study using a constructivist paradigm to analyze the experiences of trainee advocates. They used descriptive statistics to analyze quantitative data collected through surveys. Participant interviews then yielded qualitative data that they examined using team-based deductive and inductive thematic analysis. The authors applied Kern's six-step approach to curriculum development as a framework for analyzing and reporting results. RESULTS: A total of 24 participants completed the surveys, of whom 12 volunteered to be interviewed. Most survey participants were medical students who reported successful advocacy efforts despite administrative challenges. Several themes were identified that mapped to Steps 2, 4, and 5 of the Kern framework: "Utilizing Trainee Feedback" related to Needs Assessment of Targeted Learners (Kern Step 2); "Inclusion" related to Educational Strategies (Kern Step 4); and "Obstacles", "Catalysts", and "Sustainability" related to Curriculum Implementation (Kern Step 5). CONCLUSIONS: Trainee advocates are influencing the development and implementation of medical education related to the care of people with intellectual and/or developmental disabilities. Their successes are influenced by engaged mentors, patient partners, and receptive institutions and their experiences provide a novel insight into the process of trainee-driven curriculum advocacy.

Emerging Outlook on Personalized Neuromodulation for Depression: Insights from Tractography-Based Targeting.

BACKGROUND: Deep brain stimulation (DBS) has shown individual promise in treating treatment resistant depression (TRD), but larger-scale trials have been less successful. Here, we create the largest meta-analysis with individual patient data (IPD) to date to explore if the use of tractography enhances the efficacy of DBS for TRD. METHODS: We systematically reviewed 1823 articles, selecting 32 that contributed data from 366 patients. We stratified the IPD based on stimulation target and use of tractography. Utilizing two-way type III Analysis of Variance (ANOVA), Welch Two Sample t-tests, and mixed-effects linear regression models, we evaluated changes in depression severity 9-15 months post-surgery (1-Y) and at last follow-up (LFU) (4 weeks - 8 years) as assessed by depression scales. RESULTS: Tractography was used for medial forebrain bundle (MFB, n=17/32), subcallosal cingulate (SCC, n=39/241), and ventral capsule/ventral striatum (VC/VS, n=3/41) targets; and not used for bed nucleus of stria terminalis (n=11), lateral habenula (n=10), and inferior thalamic peduncle (n=1). Across all patients, tractography significantly improved mean depression scores at 1-Y (p<0.001) and LFU (p=0.009). Within the target cohorts, tractography improved depression scores at 1-Y for both MFB and SCC, though significance was only met at the alpha = 0.1 level (SCC: ß=15.8%, p=0.09; MFB: ß=52.4%, p=0.10). Within the tractography cohort, MFB with tractography patients showed greater improvement than those with SCC with tractography (72.42±7.17% versus 54.78±4.08%) at 1-Y (p=0.044). CONCLUSIONS: Our findings underscore the promise of tractography in DBS for TRD as a methodology for personalization of therapy, supporting its inclusion in future trials.

Contrasting Views of Autism Spectrum Traits in Adults, Especially in Self-Reports vs. Informant-Reports for Women High in Autism Spectrum Traits.

There is uncertainty among researchers and clinicians about how to best measure autism spectrum dimensional traits in adults. In a sample of adults with high levels of autism spectrum traits and without intellectual disability (probands, n = 103) and their family members (n = 96), we sought to compare self vs. informant reports of autism spectrum-related traits and possible effects of sex on discrepancies. Using correlational analysis, we found poor agreement between self- and informant-report measures for probands, yet moderate agreement for family members. We found reporting discrepancy was greatest for female probands, often self-reporting more autism-related behaviors. Our findings suggest that autism spectrum traits are often underrecognized by informants, making self-report data important to collect in clinical and research settings.

Investigating the relationships between resilience, autism-related quantitative traits, and mental health outcomes among adults during the COVID-19 pandemic.

Resilience is a dynamic process through which people adjust to adversity and buffer anxiety and depression. The COVID-19 global pandemic has introduced a shared source of adversity for people across the world, with detrimental implications for mental health. Despite the pronounced vulnerability of autistic adults to anxiety and depression during the COVID-19 pandemic, relationships among autism-related quantitative traits, resilience, and mental health outcomes have not been examined. As such, we aimed to describe the relationships between these traits in a sample enriched in autism spectrum-related quantitative traits during the COVID-19 pandemic. We also aimed to investigate the impact of demographic and social factors on these relationships. Across three independent samples of adults, we assessed resilience factors, autism-related quantitative traits, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. One sample (recruited via the Autism Spectrum Program of Excellence, n = 201) was enriched for autism traits while the other two (recruited via Amazon Mechanical Turk, n = 624 and Facebook, n = 929) drew from the general population. We found resilience factors and quantitative autism-related traits to be inversely related, regardless of the resilience measure used. Additionally, we found that resilience factors moderate the relationship between autism-related quantitative traits and depression symptoms such that resilience appears to be protective. Across the neurodiversity spectrum, resilience factors may be targets to improve mental health outcomes. This approach may be especially important during the ongoing COVID-19 pandemic and in its aftermath.

Heritability of quantitative autism spectrum traits in adults: A family-based study.

Autism spectrum disorder (ASD) comprises a multi-dimensional set of quantitative behavioral traits expressed along a continuum in autistic and neurotypical individuals. ASD diagnosis-a dichotomous trait-is known to be highly heritable and has been used as the phenotype for most ASD genetic studies. But less is known about the heritability of autism spectrum quantitative traits, especially in adults, an important prerequisite for gene discovery. We sought to measure the heritability of many autism-relevant quantitative traits in adults high in autism spectrum traits and their extended family members. Among adults high in autism spectrum traits (n = 158) and their extended family members (n = 245), we calculated univariate and bivariate heritability estimates for 19 autism spectrum traits across several behavioral domains. We found nearly all tested autism spectrum quantitative traits to be significantly heritable (h2  = 0.24-0.79), including overall ASD traits, restricted repetitive behaviors, broader autism phenotype traits, social anxiety, and executive functioning. The degree of shared heritability varied based on method and specificity of the assessment measure. We found high shared heritability for the self-report measures and for most of the informant-report measures, with little shared heritability among performance-based cognition tasks. These findings suggest that many autism spectrum quantitative traits would be good, feasible candidates for future genetics studies, allowing for an increase in the power of autism gene discovery. Our findings suggest that the degree of shared heritability between traits depends on the assessment method (self-report vs. informant-report vs. performance-based tasks), as well as trait-specificity. LAY SUMMARY: We found that the scores from questionnaires and tasks measuring different types of behaviors and abilities related to autism spectrum disorder (ASD) were heritable (strongly influenced by gene variants passed down through a family) among autistic adults and their family members. These findings mean that these scores can be used in future studies interested in identifying specific genes and gene variants that are associated with different behaviors and abilities related with ASD.

Gaussian Mixture Models for Probabilistic Classification of Breast Cancer.

In the era of omics-driven research, it remains a common dilemma to stratify individual patients based on the molecular characteristics of their tumors. To improve molecular stratification of patients with breast cancer, we developed the Gaussian mixture model (GMM)-based classifier. This probabilistic classifier was built on mRNA expression data from more than 300 clinical samples of breast cancer and healthy tissue and was validated on datasets of ESR1, PGR, and ERBB2, which encode standard clinical markers and therapeutic targets. To demonstrate how a GMM approach could be exploited for multiclass classification using data from a candidate marker, we analyzed the insulin-like growth factor I receptor (IGF1R), a promising target, but a marker of uncertain importance in breast cancer. The GMM defined subclasses with downregulated (40%), unchanged (39%), upregulated (19%), and overexpressed (2%) IGF1R levels; inter- and intrapatient analyses of IGF1R transcript and protein levels supported these predictions. Overexpressed IGF1R was observed in a small percentage of tumors. Samples with unchanged and upregulated IGF1R were differentiated tumors, and downregulation of IGF1R correlated with poorly differentiated, high-risk hormone receptor-negative and HER2-positive tumors. A similar correlation was found in the independent cohort of carcinoma in situ, suggesting that loss or low expression of IGF1R is a marker of aggressiveness in subsets of preinvasive and invasive breast cancer. These results demonstrate the importance of probabilistic modeling that delves deeper into molecular data and aims to improve diagnostic classification, prognostic assessment, and treatment selection. SIGNIFICANCE: A GMM classifier demonstrates potential use for clinical validation of markers and determination of target populations, particularly when availability of specimens for marker development is low.