Purpose of Review: IgA vasculitis (IgAV), formerly Henoch–Schonlein purpura, is the most common systemic vasculitis in childhood. In adults, however, this condition is poorly understood, yet associated with more severe disease and poorer outcomes. This necessitates the need for early diagnosis and management. Scope of Review: We describe the pathophysiology, clinical manifestations, and diagnosis of IgAV in adults. Poor outcomes are often due to the high frequency of glomerulonephritis in IgAV-IgA vasculitis-associated nephritis (IgAVN). We hence also aim to summarize the latest clinical data regarding treatment strategies in IgAVN. The diagnosis and differentiation in histology between IgAVN and IgA nephropathy (IgAN) remain a challenge. Review of treatment therapies: Pathological mechanisms between IgAVN and IgAN appear to be consistent between the two, and data from IgAN are often extrapolated to IgAVN. The role of various immunosuppression therapies remains controversial, and in this review, we will discuss immunosuppression use and highlight evidence surrounding emerging and promising novel therapies in IgAVN/IgAN. Our aim for this review is to guide future treatment strategies and direct future studies.

Background: The Spinal Instability Neoplastic Score (SINS) guides treatment for patients with spinal tumors, but issues arise with complexity, interobserver variability, and time demands. Large language models (LLMs) may help overcome these limitations. Objectives: This study evaluates the accuracy and efficiency of a privacy-preserving LLM (PP-LLM) for SINS calculation, with and without clinician involvement, to assess its feasibility as a clinical decision-support tool. Methods: This retrospective observational study was granted a Domain-Specific Review Board waiver owing to minimal risk. Patients from 2020 to 2022 were included. A PP-LLM was employed to maintain secure handling of patient data. A consensus SINS reference standard was established by musculoskeletal radiologists and an orthopedic surgeon. Eight orthopedic and oncology trainees were divided into two groups to calculate SINS, with and without PP-LLM assistance. LLM-predicted scores were also generated independently of any human input. Results: The main outcomes were agreement with the reference standard (measured by intraclass correlation coefficients [ICCs]) and time required for SINS calculation. The LLM-assisted method achieved excellent agreement (ICC = 0.993, 95%CI = 0.991–0.994), closely followed by the LLM-predicted approach (ICC = 0.990, 95%CI = 0.984–0.993). Clinicians working without LLM support showed a significantly lower ICC compared to both LLM methods (0.968, 95%CI = 0.960–0.975) (both p < 0.001). The LLM alone produced scores in approximately 5 s, while the median scoring time for LLM-assisted clinicians was 60.0 s (IQR = 46.0–80.0), notably shorter than the 83.0 s (IQR = 58.0–124.0) required without LLM assistance. Conclusions: An LLM-based approach, whether used autonomously or in conjunction with clinical expertise, enhances both accuracy and efficiency in SINS calculation. Adopting this technology may streamline oncologic workflows and facilitate more timely interventions for patients with spinal metastases.