Citations

Introduction The integration of telehealth into clinical care has increased access to substance use disorder (SUD) treatment. However, drug screen monitoring has largely remained tied to in-person visits, preventing a fully virtual workflow. We evaluated the acceptability, appropriateness, feasibility, and usability of video-observed oral fluid (VOOF) self-collection as an approach to address this implementation gap. Methods Participants receiving SUD treatment in outpatient ( n = 13) or residential ( n = 15) settings completed a video-recorded oral fluid self-collection protocol, followed by the Acceptability of Intervention Measure, Intervention Appropriateness Measure, Feasibility of Intervention Measure, and System Usability Scale. Results Most participants had multiple SUDs ( n = 23, 82.1%), and nearly all had co-occurring mental illness ( n = 27, 96.4%). The majority completed study measures ( n = 23, 82.1%) and returned collection kits ( n = 25, 89.2%). Acceptability, appropriateness, and feasibility all exceeded established thresholds (>3), with respective means of 3.5 (SD 1.1), 3.8 (SD 1.1), and 3.4 (SD 1.3). Usability scores were below average and more variable (mean = 51.8, SD 20.4). Scores did not significantly differ by care setting. Discussion This early-stage pilot found that VOOF self-collection was generally acceptable, appropriate, and feasible among individuals with SUD in real-world care settings. Usability scores were below average, with technical challenges related to video recording and upload likely contributing. Integrating VOOF within digital treatment platforms may streamline the user experience and improve usability while better aligning drug screen monitoring with the growth of telehealth in SUD treatment. This approach may benefit individuals experiencing transportation barriers and those in rural areas who live significant distances from testing sites.

Expert consensus recommendations from 21 academic centers were established for appropriate radiography utilization at 12 potentially low-yield anatomic sites through a multidisciplinary process endorsed by six major American medical societies.

Abstract INTRODUCTION To examine the generalizability of Alzheimer's disease (AD) biomarker models in real‐world older adults, we examined AD biomarker relationships with cognition in two multicenter cohorts that differ with respect to recruitment approach and health risk factors but were matched on a variety of characteristics. METHODS We compared harmonized health and demographic data, AD and cerebrovascular biomarkers, and cognitive performance in the community‐recruited U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) Imaging substudy and a matched sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) which recruited primarily from academic specialty clinics. RESULTS Elevated β‐amyloid (Aβ) and tau were associated with cognitive performance in ADNI but not U.S. POINTER. Findings were consistent across different cohort matching schemes, and were not explained by discrepancies in vascular risk. DISCUSSION The role of Aβ and tau in cognitive performance may be reduced in real world samples compared to academic specialty clinics.

Abstract Honoring a patient’s wishes for end-of-life care can be challenging if the patient loses decision-making capacity and the identified alternate decision-maker will not respect the patient’s wishes. This article discusses how to proceed ethically and legally when the alternate decision-maker and care team disagree about respecting a patient’s end-of-life preferences.

Abstract Background Meningiomas exhibit clinical heterogeneity. Radiotherapy (RT) remains the only adjuvant therapy, but tumor-control is variable, and biomarkers are limited. NRG/RTOG-0539 is the first prospective phase 2 trial to stratify meningioma patients for adjuvant RT based on clinical risk. Here, we apply modern molecular tools to this cohort and identify correlates of RT response. Methods Tumor tissue from 100 RTOG-0539 patients was profiled using DNA methylation arrays, RNA sequencing, and whole-exome sequencing. Recurrence scores, Molecular Groups, gene expression, and copy number alterations were compared across clinical groups and between RT responders and non-responders; non-response was defined as progression or death within 3 years. Results Modern grading criteria, including brain invasion, TERT mutation, CDKN2A/B deletion, and 1p/1q status, would reclassify 10% of tumors and alter treatment group assignment in 7%. Non-responders to RT exhibited more frequent 1p and 14q loss, and more copy number alterations. Transcriptomic and epigenetic profiling revealed immune-related signatures in responders and cell cycle–related pathways in non-responders, several of which overlapped with targets of vorinostat, a histone deacetylase inhibitor previously validated in aggressive meningioma models. The Proliferative Molecular Group was an independent predictor of post-RT recurrence in multivariable analysis, outperforming WHO grade. Conclusion Multi-omic analysis of the NRG/RTOG-0539 cohort shows that updated WHO grading criteria, incorporating molecular and cytogenetic features, improve risk stratification. However, molecular classification, particularly the Proliferative group, remains an independent and stronger predictor of RT response. These findings support integrating molecular biomarkers alongside modern grading frameworks to guide treatment and trial design in meningioma. Clinical Trial Information NCT00895622.

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk assessment after a prostate cancer diagnosis and for the care of patients with localized, regional, recurrent, and metastatic disease. These NCCN Guidelines Insights summarize the panel’s discussions on select 2026 updates to the guidelines regarding nonmetastatic disease: the elimination of the very-low-risk group, updates to the Principles of Active Surveillance, and caution in the use of focal therapy in newly diagnosed patients.

Background: Several special tests for detecting medial ulnar collateral ligament (MUCL) injuries of the elbow have been described, but data on the clinical utility of these tests are limited. Purpose: To determine the clinical utility of selected elbow special tests to detect MUCL injuries in an athletic population. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 84 consecutive patients with sports-related elbow pain underwent a standardized clinical examination. Overall, 6 special tests (moving valgus stress test, static valgus stress test, modified valgus stress test, milking maneuver, pronation flexion resistance test, and temple/cheek press test) were performed. Sensitivity, specificity, accuracy, positive/negative predictive value, and positive/negative likelihood ratio (LR+ and LR–, respectively) were calculated for individual tests and combinations of tests to detect MUCL injuries. Higher values for the LR+ and lower values for the LR– represent stronger clinical utility. Results: For detecting MUCL injuries, the moving valgus stress test resulted in the highest sensitivity (98%) and accuracy (93%) with the lowest LR– (2%). The milking maneuver had the highest LR+ (5.1) with the second highest sensitivity (77%) and accuracy (79%). The pronation flexion resistance test and the temple/cheek press test had the lowest LR+ (1.5 and 1.8, respectively) and highest LR– (94% and 95%, respectively) in addition to the lowest sensitivity (16% and 9%, respectively) and accuracy (33% and 30%, respectively). No combination of tests resulted in increased clinical utility. Conclusion: The moving valgus stress test and milking maneuver demonstrated acceptable clinical utility in which the moving valgus stress test was better for ruling out MUCL injuries and the milking maneuver was better at ruling in MUCL injuries. There was no added value when combining the 2 tests together or with other maneuvers.

ABSTRACT Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine‐learning models to real‐world data to identify clinical features that may predict clinical worsening events in PAH. Data were obtained retrospectively from the electronic health records of adults diagnosed with PAH at Mayo Clinic locations (January 2015–December 2019). Machine‐learning LASSO regularized logistic regression models were developed to analyze the association between 100 clinical features and occurrence of clinical worsening events. In total, 455 patients were included (mean age 62.1 years; 59.3% female). Of these, 232 (51.0%) experienced a clinical worsening event after a median (quartile 1, quartile 3) of 10.9 (4.1, 21.4) months. The best‐performing model had an area under the curve of 0.78, sensitivity of 78%, and specificity of 59%. The model identified 11 clinical features associated with clinical worsening events in PAH with a non‐zero coefficient: baseline age, body mass index (BMI), creatinine level, red cell (erythrocyte) distribution width (RDW), mean pulmonary arterial pressure, pulmonary vascular resistance, sodium concentration, and QT interval, along with dyspnea, number of clinic visits, and other forms of heart disease within a 1‐month window before a clinical worsening event. Baseline RDW and BMI, and number of clinic visits within the 1‐month window were statistically significantly associated with clinical worsening events. Our study used machine‐learning models to identify clinical features associated with risk of clinical worsening events in PAH. Automation of risk prediction could lead to earlier therapeutic intervention to optimize patient outcomes.

Among patients with treatment-naive hepatocellular carcinoma, glass microsphere radiation segmentectomy before liver transplant achieved high rates of complete pathologic necrosis, which was positively associated with the number of optimized treatment parameter thresholds.