Fort Belvoir Community Hospital is a Department of Defense medical facility located on Fort Belvoir, Virginia, outside of Washington D.C. In conjunction with Walter Reed National Military Medical Center, Belvoir provides the Military Health System medical capabilities of the National Capital Region Medical Directorate (NCR MD), a joint unit providing comprehensive care to members of the United States Armed Forces located in the capital area, and their families. The facility is located on a U.S. Army installation, but operates as one of the first joint service medical facilities in the U.S. military, staffed with uniformed medical personnel from the Army, Navy, and Air Force. The hospital is one of the largest medical facilities in Northern Virginia, and provides all levels of inpatient and outpatient medical care. The facility maintains a 24 hour emergency department but, like most U.S. military hospitals, transfers patients in need of a trauma center to equipped civilian medical facilities. As part of federal emergency planning in the National Capitol Region, the hospital is also tasked with maintaining unique capabilities to support continuity of government operations in the event of crisis.The $1.03 billion, 1.3 million-square-foot facility opened in August 2011, replacing Fort Belvoir's existing medical facility, DeWitt Army Community Hospital, and integrating significant portions of the former Walter Reed Army Medical Center in Washington, D.C., in accordance with 2005 Base Realignment and Closure Act. In addition to its primary facility at Fort Belvoir, the hospital also operates the DiLorenzo TRICARE Health Clinic (DTHC) at the Pentagon and satellite health centers in Fairfax and Dumfries, Virginia.
Postmenopausal women tend to experience significant changes in body composition, particularly abdominal adipose tissue (AAT) deposition patterns, which are hypothesized to be critical factors influencing future chronic disease risk. The level of protein intake to maintain or achieve a more favorable body composition for health in postmenopausal women is a central, largely unanswered question relating to the appropriateness of current dietary guideline recommendations for sufficient protein intake (set at 0.8 g/kg/day). To estimate the hypothetical effect of a range of protein intake levels on 3-year mean changes in body composition measures in postmenopausal women. We analyzed data from 3789 postmenopausal women aged 50–79 enrolled in the Women’s Health Initiative (WHI) to emulate a 3-year target trial of adhering to increasing levels of protein intake: ≥0.8 g/kg/d, ≥1.0 g/kg/d, ≥1.2 g/kg/d, and ≥1.5 g/kg/d. All participants had repeated Dual X-Ray Absorptiometry (DXA) scans with derived abdominal visceral (VAT) and subcutaneous adipose tissue (SAT). The measured differences in average levels of VAT, SAT, and other body composition measures determined at end of follow-up were estimated with the parametric-g formula. Over 3 years, hypothetical interventions of increasing levels of dietary protein intake are estimated to have dose-dependent reductions in abdominal VAT, SAT, and overall body fat, and increases in lean soft tissue, with potential benefits observed at ≥1.2 g/kg/day and the greatest estimated benefit at ≥1.5 g/kg/day of dietary protein. Compared to no intervention, if all participants hypothetically adhered to a total daily protein intake of ≥1.5 g/kg/day over 3 years, they would be estimated to have lower levels of VAT (−13.1 cm2, 95% Confidence Interval [CI] −18.9, −7.3), SAT (−25.3 cm2, 95% CI −39.7, −11.0), total body fat % (−1.0%, 95% CI −1.7, −0.3), body weight (−2.5 kg, 95% CI −3.7, −1.2) and greater lean soft tissue % (0.9%, 95% CI 0.3, 1.6) over 3 years. This hypothetical emulated intervention suggests that postmenopausal women who maintain a hypothetical total protein intake of at least 1.2 g/kg/day could experience beneficial changes in abdominal VAT, SAT, and overall body composition over three years, with even greater estimated benefits observed at an intake of 1.5 g/kg/day. These findings suggest that protein intake higher than guideline recommendations may better support healthier body composition and lower chronic disease risk in postmenopausal women.
Background:Adhesive capsulitis (AC) is a condition characterized by progressive pain and restricted range of motion, which may significantly impact patients' quality of life. It affects 2-5% of the population, with a higher prevalence in women, individuals with diabetes, and other underlying conditions. The pathogenesis involves synovial inflammation, capsular fibrosis, and neovascularization, although the initiating factors remain unclear. AC progresses through distinct phases: freezing, frozen, and thawing: each with characteristic clinical and pathological features. Methods:This review article synthesizes findings from peer-reviewed studies on adhesive capsulitis, focusing on pathophysiology, clinical presentation, and treatment outcomes. A comprehensive literature search was conducted, selecting studies based on relevance, study design, and emphasis on randomized controlled trials, systematic reviews, and meta-analyses. Results:Management options include both conservative and surgical approaches. Conservative treatment focuses on physical therapy, nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroid injections, and manipulation under anesthesia, with most patients experiencing resolution over time. Other interventions, such as ultrasound-guided hydrodilatation and arthroscopic release, may be indicated for recalcitrant cases, typically in patients with continued symptoms lasting longer than 9-12 months. Hydrodilatation, particularly capsule-preserving techniques, has shown promise in reducing pain and improving function. Arthroscopic release provides rapid pain relief and improved ROM, with long-term success demonstrated in clinical studies, though recurrence of postoperative pain may necessitate corticosteroid injections. Conclusion:Further research is needed to better understand the etiology and pathogenesis of AC, as well as to develop more effective, stage-based, and disease-modifying treatments. This approach may offer insights into targeted therapies and improved recovery in patients with AC. Level of Evidence:V.
Blood flow restriction therapy (BFRT) describes the application of an occlusive device to an extremity to occlude arterial flow while maintaining venous return. BFRT has been shown to increase strength gains and muscle hypertrophy at lower weight loads than traditionally required. It is increasing in interest among orthopaedic surgeons, as demonstrated by the number of recent studies on BFRT. There is abundant evidence of the effects distal to the occlusive cuff, but limited research on the effects proximally. Therefore, we have explored current literature regarding BFRT in the shoulder, specifically reviewing proposed mechanisms of action, applications in healthy adults, and its use for non-operative and post-operative shoulder pain. Future research opportunities are also discussed. Level of Evidence: VI.
Importance:Timely disease diagnosis is challenging due to limited clinical availability and growing burdens. Although artificial intelligence (AI) has shown expert-level diagnostic accuracy, a lack of downstream accountability, including workflow integration, external validation, and further development, continues to hinder its clinical adoption. Objective:To address gaps in the downstream accountability of medical AI through a case study on age-related macular degeneration (AMD) diagnosis and severity classification. Design, Setting, and Participants:This diagnostic study developed and evaluated an AI-assisted diagnostic and classification workflow for AMD. Four rounds of diagnostic assessments (accuracy and time) were conducted with 24 clinicians from 12 institutions. Each round was randomized and alternated between manual (clinician diagnosis) and manual plus AI (clinician assisted by AI diagnosis), with a 1-month washout period. In total, 2880 AMD risk features were evaluated across 960 images from 240 Age-Related Eye Disease Study patient samples, both with and without AI assistance. For further development, the original DeepSeeNet model was enhanced into the DeepSeeNet+ model using 39 196 additional images from the US population and tested on 3 datasets, including an external set from Singapore. Exposure:Age-related macular degeneration risk features. Main Outcomes and Measures:The F1 score for accuracy (Wilcoxon rank sum test) and diagnostic time (linear mixed-effects model) were measured, comparing manual vs manual plus AI. For further development, the F1 score (Wilcoxon rank sum test) was again used. Results:Among 240 patients (mean [SD] age, 68.5 [5.0] years; 127 female [53%]), AI assistance significantly improved accuracy for 23 of 24 clinicians, increasing the mean F1 score from 37.71 (95% CI, 27.83-44.17) to 45.52 (95% CI, 39.01-51.61), with some improvements exceeding 50%. Manual diagnosis initially took an estimated 39.8 seconds (95% CI, 34.1-45.6 seconds) per patient, whereas manual plus AI saved 10.3 seconds (95% CI, -15.1 to -5.5 seconds) and remained faster by 6.9 seconds (95% CI, 0.2-13.7 seconds) to 8.6 seconds (95% CI, 1.8-15.3 seconds) in subsequent rounds. However, combining manual and AI did not always yield the highest accuracy or efficiency, underscoring challenges in explainability and trust. The DeepSeeNet+ model performed better in 3 test sets, achieving a significantly higher F1 score than the Singapore cohort (52.43 [95% CI, 44.38-61.00] vs 38.95 [95% CI, 30.50-47.45]). Conclusions and Relevance:In this diagnostic study, AI assistance was associated with improved accuracy and time efficiency for AMD diagnosis. Further development is essential for enhancing AI generalizability across diverse populations. These findings highlight the need for downstream accountability during early-stage clinical evaluations of medical AI.
