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PURPOSE Incorporation of structured data elements within the electronic health record generates real-time data for many potential uses. However, this requires a change in documentation practices, which may increase documentation effort. The best strategies to support structured documentation interventions have not been thoroughly investigated. Herein, we report the results of extending a data capture tool across multiple disease sites and practice locations. METHODS A note-based data capture tool was developed to collect five data elements using minimum common oncology data elements language. Soft-stop close visit validation (CVV), offloading of data entry tasks to allied health staff, group education, individualized e-mails, and one-on-one assistance were used to support utilization. Baseline and follow-up surveys were collected. For each provider, we compared data capture in the 6 months before and after their CVV go-live. RESULTS Forty-five providers participated (31 physicians, 14 nurse pracationers and physician assistants [NPPAs]), resulting in 23,274 encounters pre-CVV go-live and 23,146 post-CVV go-live visits. Median percentage of all data elements captured increased from 69.5% to 82.6% across all providers. The effect of CVV was immediate and sustained. When surveyed, most respondents agreed with the value propositions of understanding practice population, outcomes, selecting trials to open, and identifying patients for trial enrollment. Most respondents (15/20, 75%) felt the soft stop was a helpful reminder. CONCLUSION Structured data can be successfully collected in provider notes with >80% data capture. Personalized assistance incorporating the structured data elements into users' preexisting templates and soft-stop CVV, along with monitoring the results of implementation and personalized follow-up, was used to support the structured data capture intervention.

Background Graft-versus-host disease (GVHD) remains a major determinant of morbidity and mortality following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Existing GVHD prediction models demonstrate modest discrimination and limited generalizability, and calibration drift across external populations is rarely characterized despite its essential role in the clinical interpretability of predicted probabilities. Objectives To develop and externally validate an explainable machine learning framework for predicting acute and chronic GVHD and associated overall survival in patients with acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and myelodysplastic syndromes (MDS) undergoing allo-HSCT, and to systematically characterize calibration across heterogeneous external validation cohorts to inform deployment requirements. Study Design The model was developed on three publicly available registry-derived datasets (N = 2,509) and externally validated across six independent cohorts (N = 14,788) comprising adult and pediatric allo-HSCT recipients, including a regional Middle Eastern cohort (UAE and Jordan). A standardized preprocessing pipeline harmonized heterogeneous datasets. Gradient boosting models (CatBoost) were used for binary GVHD prediction; exploratory overall survival analysis used a Cox proportional hazards model with predicted acute GVHD risk as a covariate. Discrimination (AUROC with bootstrap 95% CI), calibration (logistic recalibration intercept and slope with analytical 95% CI), and feature importance (SHapley Additive exPlanations, SHAP) were assessed in training out-of-fold and all external cohorts. Results In internal validation, AUROC was 0.63 (95% CI 0.61–0.65) for acute GVHD and 0.72 (95% CI 0.70–0.74) for chronic GVHD. External validation demonstrated AUROC ranges of 0.51–0.57 (acute) and 0.54–0.64 (chronic), with consistent performance across disease subgroups despite substantial heterogeneity in transplant practices and feature availability. In exploratory survival analysis, the acute-GVHD-informed Cox model achieved a training-cohort C-index of 0.679 (95% CI 0.658–0.697); external C-indices ranged from 0.47–0.53. Calibration analysis identified systematic external risk overestimation (negative calibration intercept in 10 of 11 evaluable external cohort-target combinations) with heterogeneous slope drift requiring cohort-specific recalibration. Key predictors included recipient age, graft source, conditioning intensity, GVHD prophylaxis, and HLA match ratio. Conclusions An explainable, externally validated GVHD prediction framework was developed using heterogeneous registry-derived datasets, with systematic characterization of calibration drift across multiple external cohorts, an analysis rarely reported in prior GVHD prediction literature. Predictive performance was modest for acute GVHD and moderate for chronic GVHD, constrained by missing immunobiological variables and incomplete HLA characterization. Per-cohort recalibration is required before clinical deployment, with prospective validation and benchmarking against established GVHD risk scores identified as priority next steps.

This cohort study examines patterns of bone-modifying agent use from 2013 to 2024 among men with metastatic castration-resistant prostate cancer.

21 Background: Large language models (LLMs) are becoming an increasing source of medical information for both patients and providers. Clinical decision making is occasionally complex and may use factors outside of traditional guidelines. We conducted a comparative performance evaluation of frontier models on standardized genitourinary (GU) oncology knowledge assessments derived from the American Society of Clinical Oncology Self-Evaluation Program 2025 (ASCO SEP 2025) in addition to challenging questions developed by GU oncology experts. Methods: Three frontier LLMs were selected: ChatGPT-5.2 (OpenAI, San Francisco, CA, USA), Claude Opus 4.6 (Anthropic, San Francisco, CA, USA), and Gemini 3 Pro (Google DeepMind, Mountain View, CA, USA). Model selection was based on publicly reported performance on “Humanity’s Last Exam,” a benchmark of advanced scientific reasoning (Scale AI leaderboard Jan-Feb 2026). The OpenEvidence (OpenEvidence Inc., MA, USA) search engine was also selected for evaluation. All models were asked 145 ASCO SEP 2025 GU oncology questions and 12 real world challenging scenarios using an uniform prompt. Subject matter experts peer reviewed LLM generated responses to challenging questions and credit was given only for correct rationale and option selection. Results: All models demonstrated high and comparable accuracy on Standardized knowledge assessment, with Gemini 3 Pro leading numerically at 97.24% (95% CI, 93.1% – 99.0%), OpenEvidence followed with an accuracy of 94.48% (95% CI, 89.5% – 97.3%), while Chat GPT -5.2 and Claude OPUS 4.6 scored 92.41% (95%, CI 86.9% – 95.7%) and 79.31% (95% CI, 71.9% – 85.1%) respectively. On challenging real world scenarios, ChatGPT-5.2 demonstrated the highest numerical accuracy at 100% (95% CI, 75.8% – 100%), Gemini 3 Pro and Open Evidence scored 75.0% (95% CI, 46.8%–91.1%) each, followed by Claude Opus 4.6 at 66.7% (39.1–86.3). Conclusions: All current frontier AI models appear to provide accurate medical information and rationale on a large majority of guideline based medical knowledge assessments for GU oncology. Frontier AI models also appear to have emerging competence in highly challenging real-world scenarios. AI based tools may become a valuable and reliable tool for patients and providers. Comparative LLM performance. Model ASCO SEP 2025 % (95% CI) Real World Scenarios % (95% CI) Gemini 3 Pro 97.24% (93.1–99.0) 75.0% (46.8–91.1) OpenEvidence 94.48% (89.5–97.3) 75.0% (46.8–91.1) ChatGPT-5.2 92.41% (86.9–95.7) 100% (75.8–100) Claude Opus 4.6 79.31% (71.9–85.1) 66.67% (39.1–86.3)

Importance Hypopigmented mycosis fungoides (HMF) is uncommon in adults, and data on clinicopathologic features and long-term outcomes remain sparse. Objective To investigate the clinicopathologic features and outcomes of adults with HMF in a large multicenter cohort. Design, Setting, and Participants This retrospective multicenter cohort study conducted from January 2011 to October 2023 at 6 US tertiary referral centers and included adults (18 years or older at diagnosis) with biopsy-confirmed HMF and hypopigmentation documented at diagnosis, with a median (range) follow-up of 39 (1-347) months. Data were analyzed from February to December 2025. Exposures Clinical variant, immunophenotype predominance, and polymerase chain reaction–based T-cell receptor ( TCR ) gene rearrangement in peripheral blood and/or skin. Main Outcomes and Measures Best overall response, defined as complete response, partial response, stable disease, or progressive disease, and progression to a higher stage. Results The cohort included 224 adults with HMF (median [range] age, 44 [18-74] years), of whom 164 (73%) were women (60 men [27%]), 5 (2%) were Asian individuals, 1 (0.4%) was a Hispanic/Latino individual, 1 (0.4%) was a Middle Eastern individual, 19 (8%) were White individuals, and 186 (83%) were Black individuals; 219 (98%) presented with early-stage disease. Most had hypopigmented lesions only (159 [71%]), whereas 65 (29%) exhibited mixed-variant mycosis fungoides; outcomes were similar between groups. Among 141 patients with available immunophenotyping, 88 (63%) had CD8-positive predominance. Among 207 treated patients, most (179 [90%]) received skin-directed therapy alone. Among 198 evaluable patients, the best overall response was complete response for 52 (26%), partial response for 101 (51%), stable disease for 34 (17%), and progressive disease for 11 (6%). Over a median (range) follow-up of 39 (1-347) months, 14 of 224 patients (6%) experienced progression to a higher stage, including 5 of 219 patients (2%) with early-stage disease at baseline who experienced progression to advanced-stage disease. Peripheral blood TCR monoclonality was detected in 23 of 85 tested patients (27%) and was associated with poorer treatment response but not with disease progression. Conclusions and Relevance The finding of this cohort study suggest that adult HMF demonstrated a generally indolent course, with favorable response to skin-directed therapy, irrespective of immunophenotype or mixed-variant presentation. Peripheral blood TCR monoclonality was associated with lower treatment response but not disease progression.

BACKGROUND AND OBJECTIVES: Pediatric large-volume brain arteriovenous malformations (AVMs) carry a substantial lifelong hemorrhage risk, neurological symptoms, and treatment morbidity. Single-session stereotactic radiosurgery (SRS) is often unsuitable due to constraints on dose-volume toxicity. Volume-staged SRS (VS-SRS) enables sequential dosing of large nidus volumes, potentially enhancing safety while maintaining efficacy. Evidence in children remains limited. We aimed to evaluate outcomes of VS-SRS for large AVMs in pediatric patients. METHODS: A multicenter retrospective cohort was assembled from 21 centers, including patients aged younger than 21 years treated with VS-SRS for AVMs >10 cm 3 . Clinical and radiological end points included obliteration, hemorrhage, and permanent symptomatic radiation-induced changes (RIC). RESULTS: A total of 103 patients were included (median age 14 years; IQR, 12-17). The median nidus volume at first stage was 18.2 cm 3 (IQR, 12.3-25.6). Median prescription dose per stage was 17 Gy (IQR, 16-18). The median clinical follow-up from the first stage was 57.5 months (IQR, 25-138). Obliteration occurred in 42 of 103 patients (40.8%), with actuarial rates of 6.9% (95% CI: 2.8-14) at 3 years and 29% (95% CI: 20-39) at 5 years. Hemorrhage occurred in 17 of 103 patients (16.5%) during follow-up, and permanent RIC was observed in 9 of 103 patients (8.7%). CONCLUSION: VS-SRS is a reasonably safe, selected option for pediatric large-volume AVMs when microsurgical or endovascular cure is not feasible or prudent. Delivering ≥17 Gy per stage while limiting each treatment volume to <15 cm 3 supports durable nidus control with acceptable toxicity. VS-SRS represents a key modality in multidisciplinary management of this historically difficult-to-treat population.