Citations

Background Strengthening emergency care systems could reduce death and disability in low- and middle-income countries (LMICs), yet many struggle to provide timely, high-quality care. LMICs also face growing risks from climate-related shocks and mass casualty events. This study identifies unmet emergency care needs in Nepal—one of the world’s most climate-vulnerable countries—using high-resolution geospatial data to estimate socioeconomic and climate-related inequalities. Methods We conducted a cross-sectional geospatial analysis using data from the 2022 Nepal Demographic and Health Survey (DHS), the 2021 DHS Service Provision Assessment and publicly available climate vulnerability data. Government hospitals were classified into three emergency care levels using World Health Organzation criteria: A (basic 24-hour services), B (A plus resuscitative capabilities) and C (B plus ≥50 beds and a surgeon). Household location and injury data were obtained from the DHS. Accessibility was defined as the proportion of the population within 1-hour and 2-hour travel times, estimated using AccessMod 5.8, incorporating road networks, rivers, land cover and elevation. Findings Most public hospitals (77.7%) met Level A criteria but only 49.3% met Level B and 10.8% met Level C. Nationally, 78.7% of households had 1-hour access to Level A care, 71.8% to Level B and 44.6% to Level C. Adjusted logistic regression showed rural, poorer and climate-vulnerable households had significantly lower 1-hour access compared with urban households, rural households had lower odds of access: Level A OR: 0.33 (95%CI 0.30 to 0.37), Level B OR: 0.33 (95%CI 0.30 to 0.36), Level C OR: 0.56 (95%CI 0.51 to 0.61). Households in high climate vulnerability areas had reduced access across all levels. Conclusions Substantial gaps and inequities in timely access to high-quality emergency care exist in Nepal. Rural, poorer and climate-vulnerable populations experience markedly lower access. Targeted, decentralised strengthening of emergency care capacity is essential both in Nepal and in other LMICs facing similar constraints.

Introduction: Abnormal sleep has been linked to increased dementia risk, but the pathophysiological processes driving this connection are unclear. To provide insight into the biological pathways underlying the relationship between sleep and brain health, we tested the hypothesis that increased obstructive sleep apnea (OSA) severity and shorter habitual sleep duration are associated with a higher burden of Alzheimer’s disease (AD) pathology, neurodegeneration, and neuroinflammation measured through plasma biomarkers. Methods: ARIC participants who took part in the Sleep Heart Health Study at Visit 4 (1996-98) with stored plasma samples at Visit 3 (1993-95) and Visit 5 (2011-13) were examined (n=261; mean age 61.3 years; 43.3% male). OSA severity, categorized based on the number of apneas and hypopneas per hour of sleep (<5 events/hour [Normal], 5 to <15 events/hour [Mild OSA], or ≥15 events/hour [Moderate/Severe OSA]) was assessed via in-home polysomnography, and average habitual sleep duration (<7 hours, 7 to <8 hours, or ≥8 hours) was self-reported. Plasma biomarkers of amyloid beta (Aβ) 42 and 40, phosphorylated Tau 181 (p-Tau181), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were measured using Quanterix Simoa immunoassays. Covariate-adjusted linear regression models estimated associations of sleep indices in 1996-98 with standardized plasma biomarker measurements in 1993-95 (3 years earlier) and in 2011-13 (15 years later). Covariate-adjusted linear mixed effects models quantified associations of sleep measures with the 5-year rates of change in standardized biomarker levels (1993-2013). Results: Compared to a habitual sleep duration of 8 hours or more, sleeping less than 7 hours per night was associated with higher NfL, a biomarker of neurodegeneration, 15 years later (β = 0.252; 95% confidence interval [CI]: 0.003, 0.501). Sleep indices were not associated with biomarker levels 3 years earlier. Counter to our hypothesis, Moderate/Severe OSA was associated with slower increases in p-Tau181 (β = -0.102; 95% CI: -0.198, -0.006), a biomarker of AD pathology, over a median of 17.9 years. Conclusions: Short habitual sleep duration may impact dementia outcomes through neurodegenerative and other processes rather than through pathways specific to AD pathology or neuroinflammation. Longitudinal studies with sleep measurements in larger, more diverse samples are needed to further distinguish the pathways linking sleep to dementia.

Purpose To assess the ability of artificial intelligence (AI) to identify common sports‐related pathologies using radiologic imaging and to compare ChatGPT 4.0 with 2 competitors, Grok 2 and Claude 3.5 Sonnet. Methods ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet were used. Five common orthopaedic sports pathologies were chosen: anterior cruciate ligament tears, posterior cruciate ligament tears, meniscal tears, chondral pathologies, and rotator cuff tears. Fifty images representing each pathology were collected from a radiologic imaging database when possible, which included radiographic images, computed tomography, and magnetic resonance imaging. Normal images were collected that corresponded to each diagnostic category. Receiver operator characteristic curves and area under the curve values were calculated to assess the accuracy of each AI platform. Results ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet accurately identified the pathology in 23.6%, 15.7%, and 17.1% of diseased images, respectively. ChatGPT and Grok were most accurate at identifying meniscus pathologies (ChatGPT: 48%, Grok: 42%), whereas Claude Sonnet was most accurate at identifying anterior cruciate ligament pathologies (30%). The area under the curve for ChatGPT, Grok, and Claude Sonnet was 0.21, 0.16, and 0.15, respectively (ChatGPT 4.0 vs Grok 2, P = .30; ChatGPT 4.0 vs Claude 3.5 Sonnet, P = .24; Grok 2 vs Claude 3.5 Sonnet, P > .99). There were no differences in performance between the 3 platforms overall or within any of the diagnostic categories. Conclusions ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet correctly identified the pathology in less than 25% of images of common sports‐related pathologies and showed area under the curve values well below 0.5, indicating poor accuracy. Based on these findings, we do not recommend the current use of these generative AI models for image‐based diagnosis in orthopaedics. Clinical Relevance As the use of AI becomes more popular within the general public, it becomes increasingly important to make aware the capabilities and limitations of popular AI platforms in regard to their current image‐based diagnostic capabilities.

ABSTRACT Background Cytomegalovirus (CMV) infection remains one of the most prevalent and consequential post solid organ transplant (SOT) infections. Treatment is often challenging, especially when dealing with refractory and resistant CMV infections. Methods We performed a retrospective multicenter cohort study of SOT recipients with clinically significant CMV infection (csCMVi) during 2010–2016. The primary outcome was early refractory CMV infection and secondary outcomes were drug resistance, CMV recurrence, and mortality. Analysis was done with Kaplan‐Meier, univariable logistic regression analysis, and multivariable Cox regression. Results We included 145 SOT recipients with csCMVi, majority were liver transplant (49%). Most common induction was an anti‐IL‐2 antibody (43.9%). The majority ( n = 82; 56.5%) were CMV D+/R‐ mismatch and presented asymptomatic infection (50.3%). After the initial 3 weeks of antiviral therapy, 13 (8.9%) patients had probable refractory csCMVi; most of them (10 [76.9%]) were CMV D+/R‐ mismatch. Longer time after transplant had lower risk (OR 0.68; CI 0.48–0.95, p = 0.029) while lower absolute lymphocyte count (ALC) had a higher risk of early refractory csCMVi (OR 1.54; CI 1.01–2.44, p = 0.048). Drug‐resistant csCMVi occurred in 7 patients (4.8%); a high initial CMV DNA level was associated with resistant csCMVi (OR 2.00; CI 1.08–3.93, p = 0.031). 25 patients (17.2%) experienced recurrent csCMVi within 6 months. 30 patients (20.7%) died; refractory, resistant or recurrent CMV infections were not associated with mortality. Conclusion Refractory csCMVi is associated with a low ALC and an earlier onset after transplant. Resistant csCMVi is associated with high initial CMV DNA levels. Recurrence of csCMVi is common and potentially associated with low ALC at the end of antiviral treatment.

Objective To evaluate the feasibility and potential efficacy of percutaneous spinal stimulation (epidural stimulation, ES) combined with task-specific training to reduce spasticity and improve gait and balance in individuals with progressive multiple sclerosis (MS). Methods Two men with progressive MS (EDSS 6.5) underwent ES lead implantation targeting the lower spinal cord, followed by one month of rehabilitation involving 12 ES-assisted training sessions. Assessments included instrumented gait analysis, Modified Ashworth Scale (MAS), pendulum test, and static balance testing performed at baseline, and at end of the study with ES-Off and ES-On conditions. Results Participant 1, with spastic hemiparetic gait, demonstrated improved lower extremity joint kinematics and muscle activation during gait, reduced knee extensor spasticity, and enhanced functional movement patterns with ES-On. Participant 2, with significant paraparesis and minimal spasticity, showed limited gait changes but experienced marked improvements in static balance, particularly under eyes-closed conditions. No adverse events were reported. Conclusion This pilot study demonstrates the feasibility and tolerability of ES paired with task-specific training in progressive MS. Participant-specific responses were observed, including improvements in gait kinematics, neuromuscular activation, spasticity, or balance; however, spatiotemporal gait parameters did not improve. These preliminary findings highlight response heterogeneity and the need for individualized ES parameter tuning. Trial registry name and URL: https://clinicaltrials.gov/study/NCT06019611

Retrieval-augmented generation (RAG) is rapidly emerging as a transformative paradigm for large language models (LLMs), especially in high-stakes domains like oncology that demand precision, factual grounding, and up-to-date knowledge. By pairing LLMs with external knowledge repositories, RAG systems explicitly ground model outputs in relevant retrieved documents, helping to reduce hallucinations and ensure responses reflect current evidence. In oncology, where clinical knowledge evolves continually with new research and drug approvals, RAG offers a way to integrate the latest data (e.g., trial results, guidelines, genomic databases) into decision-making. This review synthesizes the technical foundations of RAG, including its architecture and key components, and examines current applications in oncology such as clinical decision support, patient education, radiology reporting, pathology analysis, and genomics-driven precision medicine. We highlight recent studies that demonstrate RAG’s potential—for instance, improving treatment recommendations by incorporating genetic profiles and literature, and enhancing diagnostic accuracy by integrating guidelines. We also discuss emerging developments like multimodal RAG (combining text with imaging or other data), ensemble model approaches, and new explainability tools that trace model outputs to sources. Finally, we critically analyze the limitations and challenges of deploying RAG in healthcare, including computational costs, retrieval errors, noise or conflicts in retrieved information, and ethical and regulatory considerations. While RAG-based systems show promise in augmenting oncologists’ expertise with timely knowledge, careful implementation, high-quality curation of knowledge bases, and human oversight will be crucial for safe and effective adoption in clinical practice.

Abstract Background/Aims The aim of this prospective cohort study was to evaluate the extent that kinesiophobia at baseline contributed to pain intensity at follow-up after starting first line treatments in patients with a range of musculoskeletal conditions, including rheumatoid arthritis (RA), fibromyalgia (FM) and knee osteoarthritis (OA). Methods 469 patients (RA n = 156, FM n = 149, OA n = 164) recruited from the Nuffield Orthopaedic Centre, Oxford University Hospitals, participated in this study. Pain was assessed with the pain detect questionnaire, with the item average pain severity over the last 4 weeks used for the primary analysis. The Tampa Scale for Kinesiophobia (TSK) was administered to assess fear of movement. The association between TSK and pain at baseline was investigated by analysing their correlation using Pearson’s Product-Moment Correlation. RA, FM and OA groups were treated with standard care, which consisted of DMARDs, pain rehabilitation therapy, and total knee arthroplasty, respectively. The correlation between baseline TSK and change from baseline in 4-week average pain severity was calculated at 3-, 6- and 12-month follow-up using Pearson’s Product-Moment Correlation. A simple linear regression model was used for each cohort to evaluate the extent TSK contributed to change in pain from baseline. Results At baseline, mean TSK was 38.8±7.0 and mean 4-week average pain severity was 6.6±2.0 (all conditions pooled), with a Pearson’s Product-Moment Correlation of 0.28 [95% CI: 0.18-0.37], representing a small positive correlation (p &lt; 0.01) between baseline TSK and baseline pain. The results were similar for all 3 conditions independently (RA correlation 0.30 [95% CI: 0.14-0.44], FM correlation 0.31 [95% CI: 0.15-0.46], OA correlation 0.25 [95% CI: 0.06-0.41], all ps &lt; 0.01). No correlation was found between TSK and change from baseline in pain severity for the RA or FM cohorts at any follow-up. A weak negative correlation between TSK and change in pain severity was found in the knee OA group at 3 months (correlation: -0.18 [95% CI: -0.39-0.04]) and 12 months (correlation: -0.16 [95% CI: -0.39-0.08]); however, these results were not statistically significant (p = 0.12 and 0.19 respectively). The linear regression analysis showed that TSK was not associated with change in pain from baseline for any condition at any timepoint. Mean pain decreased in the RA and OA groups irrespective of TSK, with no improvement in pain seen in the FM group (RA mean pain at baseline: 6.5±2.1, at 12 months: 4.3±2.6; FM mean pain at baseline: 6.6±1.6, at 12 months: 6.6±1.8; OA mean pain at baseline: 6.5±2.2, at 12 months: 1.9±2.6). Conclusion Whilst kinesiophobia and pain severity were correlated at baseline, kinesiophobia did not negatively influence change in pain with treatment in any of the cohorts studied. Disclosure R.C. Murphy: None. A. Irani: None. E. Kelleher: None. A.J. Wall: None.