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Goal: The purpose of the research is to explore, through the lens of organizational performance and staff satisfaction, the characteristics of administrative leaders working as dyad partners with physician leaders. Methods: All 54 administrative leaders from 71 clinical departments at the three US Mayo Clinic sites were invited to participate in the study. We used an unsupervised cluster analysis machine learning method to group the leaders based on their characteristics, as measured by the 32-dimension Occupational Personality Questionnaire (OPQ 32r), and we used a three-cluster model to explore the relationships between the clusters and the performance outcome. We took the department performance data from the previous year and compared the percentage of departments with the upward changes among the clusters. For staff survey data, we calculated the percentage of departments with scores that were above average among the three clusters for both physician and administrative staff responses. Principal Findings: Analysis of personality data revealed three different clusters. Cluster 1 leaders were caring and democratic, forward-thinking, strategic, optimistic, and trusting of others. Cluster 2 leaders were extremely hardworking and authoritative. Cluster 3 leaders were caring, modest, and rule-following. Cluster 1 leaders showed the best financial performance and sense of belonging among their followers, cluster 2 leaders elicited high engagement from their departments, and cluster 3 leaders encouraged lower burnout among staff members. Practical Applications: From this study, we obtained empirical evidence of administrative leaders’ characteristics that showed positive relationships with financial and staff-satisfaction metrics. The results showed that distinct types of leaders influence administrative staff and physician staff differently and that different situations require different styles of leadership. We can also conclude that implementing robust, scientifically validated tools to assess leadership traits and tendencies can positively affect leadership and organizational performance for healthcare organizations.

BACKGROUND AND OBJECTIVES: Gynecological cancers represent 10% to 15% of cancers in women, but brain metastases (BM) are uncommon, with limited evidence regarding their management. This study investigates the role of stereotactic radiosurgery (SRS) for BM from primary gynecological cancers. METHODS: Institutions of the International Radiosurgery Research Foundation participated in this study. Inclusion criteria required histological diagnosis of epithelial ovarian, cervical, or endometrial cancer, SRS between 2000 and 2020, and at least 1 imaging or clinical follow-up. RESULTS: A total of 276 patients having SRS for 977 BM were included. Median age at SRS was 62 years (IQR, 55-70). Primary cancer origin was ovarian in 128 (46%), cervical in 43 (16%), and endometrial in 105 patients (38%). Median Karnofsky Performance Scale was 80%, and systemic disease was active in 124 (45%) of patients. A median of 1 metastasis was treated (IQR, 1-3) per patient. Median individual metastasis volume was 0.27 cc (IQR, 0.05-1.59 cc). The majority (91%) received single-fraction SRS, using a median margin dose of 18 Gy (IQR, 16-20 Gy). Actuarial overall survival was 77%, 65%, and 44% at 6, 12, and 24 months, respectively. Predictors of worsened survival included older age, cervical and endometrial primary, previous whole-brain radiation therapy (WBRT), active systemic disease, worsened Karnofsky Performance Scale, absence of subsequent surgery, and increasing number of BM. Actuarial local control was 94% at 6 months, 89% at 12 months, and 78% at 24 months. Previous SRS or WBRT, tumor bed treatment, and cervical histology increased the risk of local failure. New remote BM and leptomeningeal dissemination occurred in 44% and 11% of patients, respectively. Adverse radiation effects (ARE) occurred in 13% of cases but were symptomatic in only 3%. Previous WBRT or SRS and increased tumor diameter increased the risk of ARE. CONCLUSION: SRS is an effective management for BM from gynecological cancers with low risks of symptomatic ARE.

Abstract Clinicians struggle to accurately classify biliary strictures as benign or malignant. Current endoscopic retrograde cholangiopancreatography (ERCP)-based sampling modalities including brush cytology and forceps biopsy have poor sensitivity for pathologic confirmation of malignancy. Cholangioscopy allows for direct visualization and sampling of biliary pathology; however, this technology is also associated with inaccurate classification of biliary disease. Previously, an artificial intelligence (AI) system that analyzes cholangioscopy footage was found to be more accurate in diagnosing biliary malignancy than ERCP sampling techniques. The aim of this study was to validate this AI system on a new series of examinations. Three academic centers collected all available unedited cholangioscopy recordings. The videos were processed by the cholangioscopy AI system. After analyzing videos, the AI system provided predictions as to whether malignancy was present. AI performance in classifying strictures was compared with the performance of brush cytology and forceps biopsy. 112 cholangioscopy examinations (containing 4 817 081 images) were generated from 99 patients. Of those examinations, 61 (54.5%) were for investigation of biliary strictures (31 [50.8%] benign, 30 [49.2%] malignant). For the correct classification of strictures, the AI system was 80.0% sensitive and 90.3% specific. It was also significantly more accurate for stricture classification (85.2%) than brush cytology (52.5%; P<0.001), forceps biopsy (68.2%; P=0.04), and the combination of brush cytology and forceps biopsy (66.7%; P=0.02). A previously developed cholangioscopy AI system was found to continually outperform standard ERCP sampling modalities for accurate identification of malignancy, without additional retraining, in a multicenter validation cohort.

Three-dimensional radiomic analysis of digital breast tomosynthesis outperformed two-dimensional measures in breast cancer risk assessment.

Objectives: Breast cancer (BC) remains a critical public health issue where early detection significantly improves outcomes. Despite advancements, disparities in screening awareness and access persist, particularly in underserved populations. This study evaluates a comprehensive, technology-driven educational campaign designed to empower individuals to advocate for their breast health, bridging gaps in knowledge and accessibility. Methods: Between 10/1/22 and 12/2/22, a multiplatform campaign was conducted with IRB approval. The initiative utilized traditional media, including televised ads and educational videos, and digital tools such as a mobile app and website in English and Spanish. Central to this campaign was an interactive risk calculator, offering personalized BC screening recommendations. Data collection included user demographics, platform engagement metrics, and the identification of high-risk individuals. A cost-effectiveness analysis was conducted using campaign budget data. Results: The campaign reached 673,920 individuals through televised ads and 152,672 impressions by digital ads. The website garnered 930 unique impressions, while the app recorded 2752 downloads across 131 countries. The risk calculator was completed 637 times, identifying 72 high-risk individuals. Follow-up data revealed ongoing engagement, with the calculator used an additional 1468 times. The cost per conversion was $1.62, and the cost to identify a high-risk individual was $209.27, demonstrating remarkable cost-efficiency. Conclusions: This campaign successfully increased BC awareness and early detection efforts, leveraging a cost-effective, multiplatform approach. The integration of personalized tools like the risk calculator underscores the potential of digital interventions in health education. Future efforts should build on this model to further enhance reach and health outcomes.

Introduction Patients hospitalized for headache treatment pose unique challenges to the healthcare system. Currently, there is a lack of evidence-based guidance on management. This practice advisory aims to fill this critical gap by systematically reviewing the existing literature and providing comprehensive, evidence-based recommendations for managing headache patients during hospitalization. Methods In February 2023, the American Society of Regional Anesthesia and Pain Medicine approved this practice advisory proposal. The steering committee selected committee members based on clinical and research expertise in the field of headache medicine. Nine questions were formulated by the committee, and each question was assigned to a group composed of 3–4 members. A systematic literature search for each question was performed in Medline, Embase, Cochrane Database of Systematic Reviews and Web of Science on June 21, 2023. The results from each search were imported into separate Covidence projects for screening, data extraction, and risk of bias assessment. Additionally, relevant systematic reviews (SR) were screened. Each group submitted a structured narrative review along with statements and recommendations based on the US Preventive Services Task Force (USPSTF) format for grading of evidence. While the USPSTF framework was used, including the language in the recommendations, the formal USPSTF methodology, including the SR with meta-analysis and summary tables with forest plots, was not followed because of low overall evidence quality. The interim draft was shared electronically with each collaborator, who was requested to vote anonymously using two rounds of the modified Delphi approach. A consensus recommendation required >75% agreement. Results The panel generated 12 statements and 17 recommendations, along with their strength and certainty of evidence. Following two rounds of Delphi voting, a high consensus was achieved for all statements and recommendations. Most statements received a low-to-moderate level of certainty, and all but one recommendation received grade B or C, which was consistent with the lack of randomized controlled trials supporting most of the drugs in this document. Conclusions This evidence-based practice advisory provides a foundational step toward standardizing inpatient headache care and highlights existing gaps in the literature that should be addressed through rigorous prospective randomized studies.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.