Shelly, Shahar

Background and objectives To evaluate the safety and clinical efficacy of efgartigimod in adults with severe generalized myasthenia gravis (MG) exacerbations requiring ventilatory or enteral support. Methods Prospective, single-arm, open-label study conducted between November 2024 and April 2025. Sixteen consecutive adults with AChR- or MuSK-antibody-positive generalized MG requiring invasive or non-invasive ventilatory support or enteral feeding were enrolled; all met 2016 MG Guidelines criteria for impending myasthenic crisis. MGFA classification included Class IIIb (n=14), IIIa (n=1), and IVb (n=1). The primary endpoint was clinically meaningful improvement (CMI) in MG Activities of Daily Living (MG-ADL), defined as ≥2-point reduction from baseline at weeks 4 and 8. Results All 16 participants (mean age 58.5±17.4 years; 8 women) completed eight weeks of follow-up. At week 8, 12 patients (75%; 95% CI 47–91%) achieved the primary endpoint. Median MG-ADL decreased from 11 (IQR 9–13) to 7 (IQR 6–9) (median change −5 points; p=0.003). Median QMG decreased from 21 (IQR 19–23) to 13 (IQR 11–16) (median change −7.5 points; p<0.001), with 56% meeting QMG-CMI by week 1. Among 12 participants receiving corticosteroids (mean starting dose 21.2 mg/day), 50% reduced or discontinued by week 8. No treatment-related serious adverse events occurred. Discussion Efgartigimod produced rapid, clinically meaningful improvement in severe MG exacerbations without safety concerns. FcRn blockade may represent a practical alternative to plasma exchange or IVIG in acute care settings. Findings are limited by the absence of a control arm, which precludes definitive attribution of clinical improvement to efgartigimod alone. Clinical trials registration NCT06688253.

ABSTRACT Background Long‐term health effects of thymectomy in adults remain uncertain. Previous reports suggested associations with increased mortality, cancer incidence, and autoimmune disease, although these findings may reflect differences in patient selection, surgical indication, or follow‐up duration. We aimed to examine long‐term outcomes following adult thymectomy. Methods We performed a propensity score‐matched retrospective study of patients who underwent thymectomy or comparator cardiothoracic procedures, with follow‐up extending to 10 years. Primary outcomes were all‐cause mortality, new‐onset malignancy, and autoimmune disease. Findings were compared with those reported in a recent study that has raised caution regarding thymic excision. Results We identified 425 patients who underwent thymectomy and 5401 controls who underwent coronary artery bypass or valve surgery. After matching, 398 patients remained in each group. Thymectomy was not associated with increased mortality at 5 years ( HR = 0.63; 95% CI , 0.38–1.04) or 10 years ( HR = 0.78; 95% CI , 0.54–1.13). Excluding reclassified thymic cancers, 5‐year extrathymic malignancy incidence was 2.3% versus 1.8% ( p = 0.54); no significant differences were observed in immune‐related disease. Mortality was higher among patients with thymic malignancy. Among MG patients, only one event occurred, precluding reliable estimation. Conclusions Thymectomy was not associated with excess mortality or malignancy at 10 years compared with matched cardiothoracic controls. Indication‐stratified analyses within the thymectomy cohort showed higher mortality for thymic malignancy, with insufficient events to assess outcomes in MG patients. These findings do not support a generalized survival penalty from thymectomy, though caution is warranted for incidental thymic removal.

Abstract Most clinical large language model (LLM) benchmarks rely on clean, concise vignettes that do not reflect the noisy, long-form documentation typical of real clinical records. How LLM performance degrades under realistic chart conditions remains poorly characterised. Here we test whether structured retrieval workflows protect National Institutes of Health Stroke Scale (NIHSS) scoring accuracy under systematic context stress. Using 100 de-identified acute stroke cases and a fully crossed 4 x 4 x 3 x 3 condition matrix (144 conditions per case), we vary context acquisition method, document length, distractor load and critical-information position across four Gemini models (57,047 retained runs). Structured retrieval reduces mean absolute error (MAE) from 4.58 to 2.96 points relative to non-agentic baselines (mean gain 1.62 MAE points; 95% CI 1.57–1.67; 35% relative reduction), with consistent gains across all 36 stress combinations. Lower-cost models show disproportionately larger gains (2.76 versus 0.45 MAE points). Tool-retrieved pipelines outperform retrieval-augmented generation in 33 of 36 combinations. These findings indicate that retrieval architecture, rather than model scale alone, is a tractable lever for robust, equitable clinical LLM deployment.

ABSTRACT Background and Aims Chronic inflammatory demyelinating polyradiculoneuropathy ( CIDP ) is a common, treatable, autoimmune, neuropathy frequently misdiagnosed. The complex electrodiagnostic ( EDX ) criteria required for diagnosis are challenging to apply in routine practice. This study evaluates the effectiveness of a machine‐assisted EDX tool in real‐time identification of demyelinating nerve conduction studies ( NCS ) stipulated by 2021‐ EAN / PNS CIDP criteria in conjunction with an online CIDP clinical probability calculator. Methods We prospectively evaluated 100 consecutive patients with referral or EDX diagnosis of polyradiculoneuropathy beginning January 1, 2024. Fifty patients were included, each from Jacksonville and Rochester Mayo Clinics. Routine electromyography ( EMG ) reports were compared to a machine‐assisted support tool applying 2021‐ EAN / PNS CIDP EDX criteria to NCS assessing demyelination. The EDX ‐assistant performance was compared to the final clinical diagnosis and our previously reported online CIDP clinical prediction tool ( https://news.mayocliniclabs.com/cidp‐calculator/ ). Results Among 100 patients (60% male; median age 64), 30 had a final clinical CIDP diagnosis with 29 having intravenous immunoglobulin follow‐up; 27 (93%) improved and 2 (7%) stabilized. Routine EMGs reported demyelination in 14 of 30 (47%), with 46% sensitivity and 79% specificity. The EDX ‐assistant identified CIDP demyelination in 28 of 30 (93%), with 93% sensitivity and 57% specificity. The clinical calculator detected CIDP in 30 of 30 (100%) with 86% specificity, increasing to 94% after excluding false positives lacking demyelinating features on the EDX tool. Interpretation A real‐time machine‐assisted EDX support tool, used with a clinical web‐based calculator, streamlines CIDP assessment by improving standardized identification of 2021‐EAN/PNS CIDP demyelinating NCS criteria and aiding clinical decisions.

Introduction: We aimed to prove integration of advanced machine learning methods within a robust ensemble framework can enhance clinical decision-support for neurologists managing patients in the emergency department (ED). Methods: We engineered an ensemble framework leveraging the capabilities of the Gemini 1.5-pro-002 large language model (LLM). The model was enhanced using prompt engineering and retrieval-augmented generation (RAG). Predictive modeling achieved by combining eXtreme Gradient Boosting (XGBoost) and logistic regression for optimal accuracy in clinical decision-making. Key clinical outcomes, such as admission and mortality, were assessed. A random subset of 100 cases was reviewed by three senior neurologists to evaluate the alignment of the AI’s predictions with expert clinical judgment. Results: We retrospectively analyzed 1368 consecutive ED patients who underwent neurological consultations, assessing their clinical features, diagnostic tests, and admission outcomes. Patients admitted were typically older and had higher mortality rates, shorter intervals to neurological evaluation, and a higher incidence of acute stroke compared to those discharged. For the primary analysis (n = 250), the Neuro artificial intelligence (AI) model demonstrated significant performance metrics, achieving an area under the curve (AUC) of 0.88 for general admission predictions in comparison to actual outcomes, an AUC of 0.86 for neurological department admissions, 0.93 for long-term mortality risk, and 1 for 48 h mortality risk. Our Neuro AI model predictions showed a strong correlation with expert consensus (Pearson correlation 0.79, p < 0.001), indicating its ability to provide consistent support amid divergent clinical opinions. Conclusions: Our Neuro AI model accurately predicted hospital admissions (AUC = 0.88) and neurological department admissions (AUC = 0.86), demonstrating strong alignment with expert clinical judgment.