Citations

ABSTRACT Distinguishing atypical parkinsonian disorders (APS) from Parkinson's disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multimodel diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy‐Richardson syndrome (PSP‐RS), and PD, with the aim of identifying which dMRI model and regional metrics show the strongest group‐level separation. We analyzed 25 CBS, 42 PSP‐RS, and 21 PD participants compared to 35 age and sex‐matched controls. Using a clinically feasible 3‐shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models—diffusion tensor imaging (DTI), free‐water‐eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue‐weighted NODDI, and fixel density (FD) in fixel‐based analysis (FBA)—to comprehensively assess regional white and gray matter integrity. Group differentiation was quantified using covariate‐adjusted Cohen's d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders, and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI‐derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP‐RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free‐water‐corrected metrics showed attenuated group differences. Across clinical‐diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free‐water, multicompartment, and fixel‐based models to APS‐related neurodegeneration.

Radiation oncology is poised for a decade of unusually rapid technological and biological change. Six innovations are converging on the clinic: FLASH radiotherapy, adaptive radiotherapy with advanced imaging, artificial intelligence and machine learning in treatment planning, theranostics with targeted radionuclide therapy, spatially fractionated radiotherapy using GRID and LATTICE techniques, and continuing enhancements in particle therapy with protons and heavy ions. Each is biologically compelling, but they will not reach patients at the same speed or in equal numbers. This review proposes an evidence-and-capital framework for evaluating these innovations along four axes – physical principle, biological rationale, current clinical evidence, and dominant adoption barrier – and ranks them by anticipated population-level impact over the next five years. Capital intensity emerges as the principal determinant of diffusion: innovations layered onto existing linear accelerators (artificial intelligence, spatially fractionated radiotherapy) or onto existing nuclear medicine infrastructure (theranostics) will scale globally, whereas innovations requiring new bunkers, magnetic-shielded vaults, or dedicated accelerators (adaptive radiotherapy, FLASH, particle therapy) will remain concentrated in well-resourced centres. A low anticipated diffusion ranking is not a judgement on intrinsic clinical value: several capital-intensive modalities deliver substantial benefit for selected patients even where population-level reach is limited, and the framework distinguishes throughout between intrinsic efficacy and expected accessibility. The resulting equity gap is not primarily a scientific problem but an infrastructural one. Recognising this asymmetry should inform departmental investment, training curricula, multinational cooperative-group trial design, and health-system policy on capital subsidy and consortium models. The framework is intended for radiation oncologists, medical physicists, and cancer scientists evaluating where to invest finite institutional resources.

Purpose: The purpose of the current study was to cross-validate previously developed RMR prediction equations using laboratory measurements of RMR as a criterion. Methods: National Collegiate Athletic Association (NCAA) Division I (n = 60) and III (n = 113) male wrestlers participated in the current study (n = 173; Age: 19.6 ± 1.5 yrs.; Height: 176.3 ± 6.3 cm; Weight: 76.6 ± 10.9 kg; Body Fat: 11.4 ± 3.0%). Indirect calorimetry was used to measure RMR and Skinfold thickness was used to estimate body fat percentage (BF%) during the preseason period under standardized dress (underwear only), fasting, and resting conditions. Results: Mean measured RMR was 2102±341 kcal·d −1 . All body mass-based RMR prediction equations significantly underestimated the measured RMR, with the exception of the Jagim and Schofield equations. The Tinsley fat-free mass (FFM) equation and the Cunningham equation had the lowest constant error (CE) values when compared to measured RMR (mean bias ± standard error: 80 ± 20 kcal and 126 ± 20 kcal·d −1 , respectively). Linear regression indicated the Tinsley FFM (R 2 = 0.422, RMSE = 279 kcals, %RMSE = 13.3%), and Cunningham FFM equations had the closest agreement to measured RMR (R 2 = 0.422, RMSE = 301 kcals, %RMSE = 14.3%), along with the Jagim equation for a body mass-based prediction equation (R 2 = 0.377, RMSE = 338 kcals, %RMSE = 16.1%). The standard error of the estimate (SEE) ranged from 260 to 272 kcal·day −1 across equations. Coefficients of determination (R 2 ) ranged from 37% to 42%. The new body mass and FFM-based equations exhibited good agreement based on the CE (26 and -13 kcal·day −1 ), RMSE (274 and 252 kcal·day −1 ), respectively. Conclusions: The newly developed equations demonstrated mean bias <2% of measured RMR.

Introduction: Financial literacy is an often-overlooked aspect of medical education. Prior research indicates that trainees frequently lack knowledge in areas such as loan repayment, retirement planning, and investing, despite reporting strong interest in financial education. Insufficient financial literacy may contribute to financial stress, burnout, and suboptimal long-term planning, highlighting the need for targeted curricula. Methods: We sent a cross-sectional survey to 446 medical students across all Mayo Clinic Alix School of Medicine campuses. One hundred thirteen recipients responded (25% response rate). Participants completed the abbreviated five-item Consumer Financial Protection Bureau (CFPB) financial well-being scale, five previously published “Big 5” objective financial literacy questions, five investigator-developed medical training-specific knowledge questions, and seven investigator-developed self-assessed financial literacy items. We used descriptive statistics to summarize responses. We used analysis of variance, t tests, and regression analyses to assess differences across demographic groups. Results: Despite moderate literacy in all three measured domains, we identified gaps of knowledge regarding loan repayment and retirement planning. Students scored higher on “Big 5” items (3.91/5) than medical training-specific items (3.48/5; P = 0.001). No significant differences emerged across age, campus, or training year for financial well-being or objective literacy. Perceived and objective literacy were moderately correlated (r = 0.52, P&lt;0.001). Interest in additional financial education was high (81%). Conclusion: Despite moderate financial literacy on basic concepts, medical students exhibit notable gaps in knowledge regarding loan repayment and retirement planning. These findings persist across all years of training, highlighting the need for structured, early financial education to better prepare students for long-term financial security.

PURPOSE To evaluate open-source large language models (LLMs) for extracting cancer-specific phenotypic data, benchmark their performance against GPT4 models, and assess the impact of fine-tuning with training data sizes. METHODS Open-source LLMs (Mistral, LLaMa, MAMBA, BioMistral) were evaluated in zero-/one-shot and fine-tuned setups against GPT4-turbo/GPT4o to extract the cancer presence, progression, response, and metastatic sites from radiology impressions of patients with solid tumors treated at Dana-Farber Cancer Institute. Performance metrics (accuracy, precision, recall, F1-score) were computed. McNemar's odds ratio (OR), measuring which model is more likely to be correct when they disagree, was computed with 95% CI. Statistical significance was assessed using the alpha of .000139. RESULTS This study included 2,623 patients (25,273 radiology impressions). In zero-/one-shot settings, GPT4-turbo/GPT4o outperformed open-source LLMs. However, fine-tuned open-source LLMs achieved higher F1-scores than GPT4 models. Compared with the best-performing GPT4 model, fine-tuned Mistral0.2-7.3B (OR, 0.27 [95% CI, 0.20 to 0.36]; P < .00001), Mistral0.3-7.3B (OR, 0.26 [95% CI, 0.19 to 0.36]; P < .00001), LLaMa2-6.7B (OR, 0.30 [95% CI, 0.22 to 0.40]; P < .00001), LLaMa3.1-8B (OR, 0.37 [95% CI, 0.28 to 0.48]; P < .00001), and MAMBA-2.8B (OR, 0.32 [95% CI, 0.24 to 0.42]; P < .00001) showed significantly better performance in ascertaining disease progression. Performance was consistently better for inferring overall response, any evidence of cancer, and sites of metastases, with no significant differences among fine-tuned open-source LLMs. Fine-tuning gains plateaued at 25% of training data (5,718 impressions) and remained comparable at 5% (1,144 impressions). CONCLUSION Open-source LLMs, when fine-tuned using labeled data, can effectively automate the ascertainment of key radiophenotypic variables using only the impression section of radiology reports, without the full report text. Their consistent performance in small training sets suggests that these models may provide a scalable approach for phenotypic characterization of patients with cancer in real-world clinical settings.

Introduction: Inclusive hair and skin care are essential for culturally congruent health care; however, hospitalized patients with textured hair and skin of color experience limited access to appropriate products. Nursing staff also report gaps in knowledge related to culturally responsive care of hair and skin. This initiative aimed to improve care through product access and education. Methods: A quality improvement project was conducted at a Midwestern academic medical center. Surveys of nurses, patient care assistants, and employee resource groups informed product selection and education design. Interventions included stocking products and providing education supported by a webpage and video library. Results: Post-education evaluations showed increased knowledge of hair and skin care, along with improved confidence and ability to provide culturally sensitive care. Discussion: Addressing hair and skin care through access to products and education enhances patient care. This model offers a replicable framework to improve inclusive hair and skin care practices.

Acute cholecystitis affects ∼200,000 individuals annually in the United States, with laparoscopic cholecystectomy as the gold standard treatment. However, in high-risk surgical candidates, alternative drainage methods are necessary. Percutaneous transhepatic gallbladder drainage (PT-GBD) has traditionally served as the primary alternative intervention, offering rapid decompression, but is limited by a negative impact on the patient’s quality of life, the risk of long-term or even permanent tube placement, adverse events, and high recurrence rates. Endoscopic approaches, such as endoscopic transpapillary gallbladder drainage (ET-GBD) and, more recently, endoscopic ultrasound-guided gallbladder drainage (EUS-GBD), have emerged as viable alternatives to PT-GBD. EUS-GBD, first introduced in 2007, offers technical and clinical outcomes comparable to PT-GBD with fewer adverse events, shorter hospital stays, and lower rates of recurrence. The introduction of lumen-apposing metal stents (LAMS) has revolutionized EUS-GBD, simplifying deployment and enabling subsequent internal gallbladder access, which allows additional interventions such as cholecystoscopy and stone removal. EUS-GBD is now supported by international guidelines, and 1 device has achieved FDA approval for the management of acute cholecystitis in nonsurgical candidates. Proper patient selection is essential, guided by multidisciplinary evaluation, with EUS-GBD contraindicated in specific scenarios such as gallbladder perforation, coagulopathy, and large-volume ascites. Technical considerations include choice of access site (transgastric versus transduodenal), stent type, and procedural route [direct (freehand) or wire-guided]. Postprocedural care and stent management remain variable and nonstandardized. Emerging data suggest that interval cholecystectomy can still be performed safely after EUS-GBD. As adoption of EUS-GBD with LAMS expands, structured training and standardization of practice are crucial to optimizing outcomes.

An accurate differential diagnosis for diffuse lung disease can be confidently established by using a systematic approach to identify the predominant radiographic pattern, determine the distribution of disease, and assess for architectural or volume change.