Panchawagh, Suhrud

OBJECTIVE Essential tremor (ET) is common and disabling in older adults. Many patients aged ≥ 80 years are ineligible for deep brain stimulation due to medical comorbidities. Data on noninvasive alternatives, such as MR-guided high-intensity focused ultrasound (MRgHIFU), remain limited in this population. The aim of this study was to evaluate the safety and efficacy of unilateral MRgHIFU thalamotomy targeting the ventral intermediate nucleus (VIM) for the treatment of ET in patients ≥ 80 years of age. METHODS This retrospective multicenter cohort study included consecutive patients aged ≥ 80 years with medically refractory ET treated with unilateral MRgHIFU VIM thalamotomy between 2016 and 2023 at five academic neurosurgical centers. All patients met skull density ratio criteria and underwent standardized targeting. Clinical assessments were conducted at baseline, immediately posttreatment, and at the 3-month follow-up. The primary outcome was change in tremor severity assessed by using the Tremor Research Group Essential Tremor Rating Assessment Scale (TETRAS). Secondary outcomes included the Clinical Rating Scale for Tremor (CRST) score, Dynamic Gait Index score, patient-reported tremor relief, and adverse events. RESULTS One hundred twenty-nine patients (76 male, mean age 84 years) were included. At 3 months, the mean TETRAS score improved by 9.2 points (p < 0.001). CRST scores also showed significant improvement. The mean patient-reported tremor relief was 81%. Adverse events were mild and transient, with a 1.6% rate of nonsurgical adverse events. Subgroup analysis comparing patients 80–84 years versus those ≥ 85 years of age showed no significant differences in treatment response. Gait outcomes improved modestly but were not significant in the age ≥ 85 years group. CONCLUSIONS Unilateral MRgHIFU thalamotomy is a safe and effective noninvasive treatment for ET in patients ≥ 80 years of age. It results in significant improvements in tremor and disability, with a low complication rate. These findings support its consideration for older patients who are ineligible for invasive procedures and highlight the importance of age-specific metrics in neuromodulation outcomes research.

Abstract INTRODUCTION Alzheimer's disease (AD) is a progressive neurodegenerative disorder that needs better predictive tools. Using the National Alzheimer's Coordinating Center Uniform Data Set, this study developed machine learning (ML) models and a practical clinical tool for AD prediction. METHODS Data from 52,537 individuals (22,371 with AD) and more than 200 variables were processed with MissForest imputation and genetic algorithm‐based selection. Multiple ML models were trained, and interpretability was performed using SHAP and permutation importance. A LightGBM model was refined through iterative backward feature elimination (IBFE) followed by manual refinement. RESULTS LightGBM performed best (receiver operating characteristic‐area under the curve [ROC‐AUC] 0.91, accuracy 82.0%). Key predictors included arthritis, age, body mass index, and heart rate. A 19‐feature model retained accuracy (81.2%) and ROC‐AUC (0.90). DISCUSSION This lightweight tool predicts AD using mostly routine variables. Limitations include its cross‐sectional nature, and would need external validation. An interactive web app and GitHub resource are available. Highlights Developed a lightweight ML based tool using 19 routinely available features. The lightweight model achieved an ROC-AUC of 0.90 for Alzheimer's disease prediction on NACC multicenter data. Genetic algorithm, IBFE, and manual refinement enabled optimal feature selection. Tool hosted on an open‐access platform for clinical and research use. SHAP analysis provided model interpretability and feature‐level insights.

Abstract Background: Conventional null hypothesis significance testing, which relies heavily on p-values and confidence intervals, remains central to scientific research. However, both measures are subject to variability and may yield fragile or misleading conclusions. This has contributed to reproducibility concerns and the need for more informative inferential tools. Methods: We propose the Replicative Significance Index (RSI), a simulation-based metric that estimates the proportion of hypothetical replications that achieve statistical significance under identical design assumptions. The RSI directly models the stability of observed significance, in contrast to post hoc p-value interpretation or traditional power analysis. We further introduce a tail-augmented RSI (RSI*), which incorporates the depth of significant p-values through a tail-dominance adjustment. RSI and RSI* were implemented in R and demonstrated via conceptual examples, simulations under varying precision, and illustrative case scenarios of fragile significance and hidden robustness. Results: The simulations revealed that the RSI discriminates between superficially similar p-values that differ markedly in replicability and highlights cases where confidence intervals and p-values may overstate or understate robustness. RSI* further rewards distributions concentrated in the lower tail, distinguishing robustly reproducible findings from borderline significance. An R package and interactive Shiny web application are provided to facilitate implementation.