Citations

ABSTRACT Braak and others have proposed that Lewy body pathology (LBP) in Parkinson’s disease (PD) may arise not only in the brain but alternatively from an initial site in the gastrointestinal (GI) tract with subsequent passage to the central nervous system CNS through the vagus nerve or other routes. We tested this hypothesis by using both immunohistochemistry (IHC) and RT QuIC a form of alpha synuclein seed amplification assay (SAA) to detect alpha synuclein LBP in samples from selected brain regions and 10 GI tract sites taken from autopsies of 50 PD subjects and 128 elderly subjects without parkinsonism or dementia including 34 with IHC identified CNS incidental Lewy body disease (ILBD) and 94 with no Lewy body IHC pathology detected (NLB). A positive SAA or IHC result was restricted to the GI tract in only 2 subjects while LBP by either SAA or IHC was restricted to the brain in 11 subjects. To fairly compare GI-only with brain-only synucleinopathy, however, we would have to do SAA on brain samples from all ILBD and NLB cases in at least 4 critical brain regions: olfactory bulb, medulla, pons, and amygdala. Further SAA of brain regions is estimated, based on the proportional results to date, to potentially identify 21 additional brain-only LBP subjects, for a total of 32, if it were done on all of the NLB subjects. From this brain-only LBP is estimated to be 16 times more common than GI-only LBP. To assess the clinical impact of SAA-positive GI sites we found that the number of positive sites per subject is significantly correlated with UPDRS motor score and SCOPA-AUT GI related scores including those for salivation, straining, constipation, and bowel movement.

Abstract Introduction Evaluate large language models (LLMs) for scoring medical student essays, and compare various prompting techniques and models. Methods OpenAI GPT scored 51 medical student reflection essays (15 real, 36 fabricated) using a previously-reported 6-point rubric (April-May 2025). We compared 29 prompt-model conditions by systematically varying the LLM prompts (including the persona, scoring rubric, few-shot learning [exemplars], chain-of-thought reasoning, and temperature), fine-tuning, and model (including GPT-4.1, GPT-4.1-mini, GPT-o4-mini, and GPT-4-Turbo). Outcomes were accuracy (compared with human raters, measured using single-score intraclass correlation coefficient [ICC] and mean absolute difference [MAD; zero indicates perfect agreement]), within-condition reproducibility, and cost. Results Across all conditions, it took mean (SD) 3.73 (3.12) seconds to score 1 essay. The cost to score 100 essays was USD $0.04 for GPT-4.1-mini, $0.21 for GPT-4.1, $0.57 for GPT-4.1 with 3 exemplars, and $2.00 for fine-tuned GPT-4.1. When the one-time cost of fine-tuning was amortized across 10,000 essays, the cost for fine-tuned GPT-4.1 was $0.20 per 100. Accuracy was “almost perfect” (ICC >0.80) for 28/29 conditions (97%). Fine-tuned models were more accurate than non-fine-tuned models (MAD difference –0.24 [95% CI, –0.34, –0.14]). Conditions with exemplars were more accurate than those without (MAD difference –0.44 [CI, –0.57, –0.31]). Accuracy progressively decreased as 6, 3, 1, and 0 rubric levels were explicitly defined in the prompt (P<.001). Contrary to hypotheses, accuracies for chain-of-thought prompts and variations in temperature and persona were not significantly different from the baseline prompt. Reproducibility ICC was >0.80 for 28/29 conditions (97%). Discussion Automated LLM essay scoring demonstrated near-perfect accuracy and reproducibility for most prompt-model conditions. Fine-tuned models and prompts with exemplars had higher accuracy but higher cost. Fine-tuned models had lower per-essay costs for larger essay volumes. For smaller volumes, non-fine-tuned GPT-4.1 provided excellent results at moderate cost. GPT-4.1-mini provided very good results at low cost.

Hypertension is the main risk factor for cardiovascular disease (CVD), affecting 20–40% of Latin American (LATAM) adults, and responsible for more than two million deaths annually due to CVD. The different ethnic, economic, geographic, and cultural characteristics of the LATAM population influence the high prevalence of all cardiovascular risk factors (CVRF), particularly metabolic disturbances such as type 2 diabetes (DM2), obesity and the metabolic syndrome. Their main determinants in LATAM includes environment, food quality, social inequity, low education, political aspects, contextual behaviour, and genetics. The prevalence of overweight and obesity in LATAM increased during the last four decades reaching figures of 10–20% in childhood, 30–40% in adolescence, and 60–70% in adults. Many studies in the region have reported the extremely low rates of awareness, treatment, and control of CVRF in the general population of LATAM, particularly in patients with metabolic disorders, and the consequent high cardiovascular morbidity and mortality. This 2026 LATAM consensus is developed by a large group of experts from different LATAM countries, the USA and Europe, representing areas of internal medicine, cardiology, nephrology, endocrinology, geriatrics, paediatrics, pharmacology, and epidemiology. A careful search for novel studies in LATAM, together with new evidence that has emerged since the 2019 LATAM consensus, support the statements and recommendations in the current report. This update aims to provide clear and useful recommendations for health professionals to improve awareness, treatment, and control of hypertension and associated CVRF in the region.

Abstract Despite effective control of inflammation in rheumatoid arthritis (RA), persistent and severe pain remains a challenge. Dysregulated pain processing in the central nervous system may underlie this disconnect. The PainDETECT questionnaire, originally developed to identify neuropathic pain, may capture features of central sensitization. We hypothesized that neuropathic-like pain at diagnosis predicts worse pain prognosis in early RA, independent of baseline pain severity and inflammation. We also explored brain activation patterns using neuroimaging. In this prospective cohort study, adults with newly diagnosed RA completed the PainDETECT questionnaire at baseline. Bodily pain was measured using the SF-36 Bodily Pain Subscale at baseline, 3, 6, and 12 months. C-reactive protein (CRP) was monitored. Linear mixed-effects models examined associations between PainDETECT, as a continuous measure, and bodily pain and CRP over time, adjusting for baseline outcome values. A neuroimaging substudy (n = 27) used task-based functional MRI to assess brain responses to evoked pressure pain. Among 158 participants (mean age 58.4 years; 35% male), 24.7% had likely neuropathic-like pain (PainDETECT ≥19) at baseline. Higher baseline PainDETECT scores were associated with worse bodily pain throughout follow-up in fully adjusted models (β = −0.43; 95% CI: −0.84 to −0.02; P = 0.043), independent of socio-demographics and comorbidities. No association was found between PainDETECT and CRP. On neuroimaging, higher PainDETECT scores correlated with increased activation in the left insula, dorsal anterior cingulate cortex, and left amygdala during evoked wrist pain. Neuropathic-like pain at diagnosis predicts a worse pain prognosis in early RA, independently of inflammation. Early identification may support more targeted and effective pain management.

Background: We aimed to develop automated data extraction pipelines with large language models (LLMs) to extract registry data from total hip arthroplasty (THA) operative notes and compare the performance with that of existing natural language processing (NLP) algorithms. Methods: We randomly sampled 1,000 primary THA cases from our institutional registry. Two human annotators manually reviewed each operative note for 3 data points: surgical approach, bearing surface, and fixation technique. All labeled THA notes were split into the development set (n = 239) and the testing set (n = 719). We developed a custom data extraction pipeline for each data point by combining an iteratively customized prompt with an LLM. The performance was compared with that of existing rules-based NLP algorithms. Results: The accuracy of LLMs was superior to that of NLP algorithms for all data points: surgical approach (96% compared with 94%), bearing surface (89% compared with 74%), and fixation technique (96% compared with 95%). Furthermore, the LLM accurately inferred the bearing surface for 80% of the notes that were ambiguous about the bearing surface. Conclusions: We developed LLM pipelines for extracting 3 registry-relevant data points from THA operative notes, demonstrating superior performance to existing NLP algorithms. Clinical Relevance: LLMs have the potential to impact clinical care, including the evaluation of electronic medical record free-text data. As registries serve as a cornerstone of orthopaedic evidence, this work demonstrates promise for LLMs to simplify, improve, and democratize the construction of registry databases from operative notes.

Background: Successful applications of artificial intelligence (AI) in healthcare have increased interest in how it could be integrated into orthopaedic surgery. However, orthopaedics surgeons’ current use of AI and attitudes toward its incorporation remain largely unexplored. The 2025 American British Canadian Travelling Fellowship Survey on AI in Orthopaedic Surgery aimed to assess AI use, knowledge, training, and attitudes among orthopaedic surgeons. Methods: The survey was administered from May 7 to September 4, 2025 to orthopaedic surgeons across Australia, New Zealand, the United Kingdom, and North America. Questions addressed demographics, AI training and use, and attitudes toward AI. Descriptive statistics summarized responses, chi-square tests compared AI use by geography and attitudes by career stage, and linear regression explored associations between demographics, AI behaviors, and outlook on AI. Results: Among 350 participants, most were trainees (45.1%) and from North America (42.9%). AI use exceeded training, and 79.9% rated their AI knowledge as average or poor. Trainees reported significantly more favorable attitudes toward AI than late-career respondents. Higher self-rated AI knowledge and AI use in clinical and research settings were associated with a more positive outlook on the future of AI in orthopaedics. Conclusions: These findings highlight the need for formal AI education, as use is widespread despite limited training, particularly among later-career surgeons. Future studies should reduce self-selection and career-stage imbalance and include additional demographic variables to improve generalizability. Level of Evidence: III . See Instructions for Authors for a complete description of levels of evidence.

Parkinson’s disease (PD) significantly affects patients’ quality of life through debilitating motor symptoms, such as Freezing of Gait (FoG). Continuous, in-home monitoring of FoG is essential for timely clinical intervention but remains challenging due to high power consumption, annotation cost, and the controlled environments required by current wearables. We introduce LIFT-PD (the source code is available at: https://github.com/shovito66/LIFT-PD ), a novel self-supervised learning (SSL) framework for real-time, patient-independent FoG detection that uniquely utilizes a single waist-worn accelerometer—an approach traditionally considered less optimal due to weaker gait signatures. LIFT-PD leverages SSL on unlabeled data collected from uncontrolled, real-world settings and employs a novel Differential Hopping Windowing Technique (DHWT) to address gait variability and dataset imbalance. Additionally, an opportunistic inference module selectively activates the deep learning model only during patient movement, significantly reducing power consumption and enabling continuous monitoring ( \(>\) 48 hours). Experimental results show that LIFT-PD achieves a 7.25% increase in precision and 4.4% improvement in accuracy compared to supervised and semi-supervised baseline models while requiring approximately 40% fewer labeled training samples. Evaluations across diverse patient characteristics-including severity, medication state, age, and gender-confirm the model’s robustness and clinical applicability, positioning LIFT-PD as a practical, energy-efficient, and scalable solution for continuous real-world FoG monitoring in PD.

Importance Exploring the relationship between moral distress and occupational burnout is necessary to understand the association between these constructs. Objective To evaluate moral distress among physicians and US workers, and to explore the association of moral distress with burnout, intent to leave (ITL) current position, and intent to reduce clinical work hours (ITR). Design, Setting, and Participants This cross-sectional national survey study included physicians from all specialties and a probability-based sample of employed nonphysicians. Participants were aged 29 to 65 years. Data were collected between October 19, 2023, and March 5, 2024. Data were analyzed from June 30 to October 20, 2025. Main Outcomes and Measures Moral distress was measured using the Moral Distress Thermometer (MDT, range 0-10), with a high level of moral distress defined by a score of 4 or higher. Burnout was measured using the complete Maslach Burnout Inventory (MBI). Professional fulfillment was measured using the Stanford Professional Fulfillment Index. ITL and ITR were measured using a standardized item with response options of none, slight, moderate, likely, and definitely. Results This survey study included 5741 physicians and 3501 nonphysician US workers. The median (IQR) age of physicians was 53 (44-62), and included 3262 men (58.0%), 2255 women (40.1%), and 107 individuals who responded other (1.9%). The mean (SD) moral distress score for physicians was 3.29 (2.81), with 2243 (39.1%) reporting a high level of moral distress (4 or more considered high). On multivariable analysis, women physicians had higher odds of moral distress (OR, 1.29; 95% CI, 1.12-1.48). Compared with internal medicine subspecialists, emergency medicine physicians (OR, 3.16; 95% CI, 2.27-4.4) and general internal medicine physicians (OR, 1.92; 95% CI, 1.42-2.59) were more likely to report high levels of moral distress. Mean emotional exhaustion and depersonalization scores, as well as the proportion of physicians with burnout, were higher with each 1-point increase in moral distress score. The overall correlation between the emotional exhaustion score and moral distress score was R = 0.55 ( P &amp;lt; .001) while the correlation between the depersonalization score and moral distress score was R = 0.50 ( P &amp;lt; .001). Additionally, 1068 of 3477 physicians (30.7%) with a moral distress score less than 4 had burnout symptoms compared with 1675 of 2231 physicians (75.1%) with scores of 4 or more ( P &amp;lt; .001). The prevalence of ITL and ITR was higher for each 1-point increase in moral distress score. For example, 619 of 3404 physicians (18.2%) with low moral distress reported ITL within 24 months compared with 748 of 2171 (34.5%) among those with high moral distress ( P &amp;lt; .001). Compared with other US workers, physicians had markedly higher odds of experiencing moral distress (OR, 4.40; 95% CI, 3.84-5.06). Conclusion and Relevance In this survey study, moral distress was common among physicians and experienced at higher rates than the general US working population. Understanding the differences between moral distress and burnout may allow organizations to more effectively implement interventions to address both concerns among clinicians.

Nodular fasciitis is a rapidly growing soft tissue tumor that typically arises in children and young adults. Histologically, it consists of short, sweeping, and intersecting fascicles of bland-appearing myofibroblasts. The majority of nodular fasciitis lesions harbor rearrangements involving the ubiquitin-specific protease 6 ( USP6 ) gene. Most lesions are benign, with a tendency to regress spontaneously or remain stable following conservative excision. However, rare cases have shown clinically aggressive behavior, including distant metastasis. More recently, morphologic variants with malignant features have also been reported. We present a 20-month-old boy who developed a rapidly enlarging facial mass following blunt trauma. The excised mass revealed a variant of nodular fasciitis displaying marked cellular pleomorphism and frequent atypical mitotic figures. Molecular analysis identified a COL3A1::USP6 fusion and a TP53 Y234C mutation. While the majority of facial lesions referred to plastic surgeons for excision are benign, this case highlights the need for a broad differential diagnosis that includes both benign and potentially malignant entities. It also emphasizes the importance of multidisciplinary evaluation and management in patients with complex and unusual pathologic diagnoses.