Citations

Abstract Background Opportunistic bilateral salpingectomy (OBS), the removal of fallopian tubes during hysterectomy or instead of tubal ligation, is recommended to reduce ovarian cancer risk. However, data on the effectiveness of prophylactic OBS is limited. Methods This population-based retrospective cohort included individuals aged 18–80 in Ontario, Canada, who underwent OBS or comparator surgeries (hysterectomy with ovarian and tubal conservation or tubal ligation) from 2011 to 2023. Individuals with gynecologic cancer before or within six months of surgery were excluded. Hazard ratios and 95% confidence intervals were estimated using Cox proportional hazards regression. Sensitivity analyses varied the time between surgery and cancer diagnosis to account for pre-existing serous tubal intraepithelial carcinomas. Results Among 196,839 individuals, 57,941 underwent OBS and 138,898 underwent comparator surgeries. 142 epithelial ovarian cancers were diagnosed (19 in the OBS group and 123 in the comparator surgery group). OBS was associated with reduced risk of any ovarian carcinoma, with an adjusted hazard ratio (aHR) of 0.60 (95% CI, 0.36 to 0.98). For serous ovarian carcinoma (high- and low-grade combined due to missing grade data), the aHR was 0.47 (95% CI, 0.22 to 0.99). Sensitivity analyses with 12- and 18-month intervals showed aHRs of 0.59 (95% CI, 0.35 to 0.99) and 0.60 (95% CI, 0.35 to 1.01) for all ovarian carcinomas, and 0.49 (95% CI, 0.23 to 1.01) and 0.47 (95% CI, 0.22 to 0.99) for serous ovarian carcinoma, respectively. Conclusions OBS was associated with reduced risk of ovarian carcinoma, supporting its role as a primary prevention strategy.

Background The management of osteochondral lesions of the talus (OLT) is challenging, with 2 main options being conservative and surgical management. Orthobiologic therapies come to bridge the gap between these 2 options; however, inconsistencies in indication, composition, and heterogeneous evidence limit clinical decision-making. To address these challenges, 3 International Congress Meetings on Cartilage Repair of the Ankle were convened in Pittsburgh, USA (2017), Dublin, Ireland (2019), and New York, USA (2025) to establish agreement on the role of conservative management and injectable orthobiologic strategies in the treatment of OLT. Methods Seventy-five multidisciplinary experts from over 25 countries and 1 territory participated in a structured Delphi-based consensus process. Blinded surveys, structured questionnaires, and in-person discussions were used to develop and refine statements, supported by literature review and graded evidence (A1–E). Consensus strength was defined as consensus (51%-74%), strong consensus (75%-99%), or unanimous (100%) Results In total, 29 consensus statements were achieved across the 3 meetings. Strong agreement was reached regarding indications and expected outcomes of conservative management, including the role of immobilization and appropriate timing for surgical intervention. Orthobiologic injections, including platelet-rich plasma, concentrated bone marrow aspirate, and hyaluronic acid, were considered appropriate in select patients with persistent symptoms, although no superiority was demonstrated among formulations, preparation methods, or injection strategies. When used as adjuncts to surgical procedures, orthobiologics may improve radiological outcomes, with limited evidence for improvement in clinical outcomes. Technical considerations, including preferred harvest sites and a lack of standardized preparation protocols, were identified, along with key principles for patient counseling Conclusion These international consensus statements establish a standardized framework for the conservative management and use of injectable orthobiologic therapies in OLT. By clarifying indications, technical considerations, and current limitations, this work aims to improve consistency in clinical practice, guide treatment decision-making, and inform future research.

ABSTRACT Part I of our three‐part series outlines the essential technical and operational components required for successful digital pathology implementation including hardware, software, and IT infrastructure. It reviews the rationale for digitization, highlighting improvements in workflow efficiency, diagnostic collaboration, and preparedness for computational applications. Key components of Whole Slide Imaging (WSI) systems are discussed, including practical considerations for scanner selection, scanning modalities, and performance trade‐offs relevant to dermatopathology. The article also examines image compression strategies and pyramid image representation, with attention to balancing image quality and storage efficiency. Finally, we review DICOM standards and storage infrastructure options, including cloud, on‐premises, and hybrid models, and associated network bandwidth requirements.

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.