Citations

Background Burnout arises from prolonged, unresolved work-related stress and adversely affects professional performance and overall well-being. Resident doctors and medical students are particularly vulnerable because of the demands of balancing clinical, academic and related responsibilities. Aims This study aimed to assess self-reported burnout and related stressors among UK resident doctors and medical students, and estimate the economic impact of burnout-related absenteeism among resident doctors. Method Self-report questionnaires assessing burnout and related stressors were distributed to UK resident doctors and medical students. A total of 2793 responses were received: 1439 (51.5%) from resident doctors and 1354 (48.5%) from medical students. Economic losses from absenteeism were calculated with a human capital approach. Results Burnout was reported by 70% ( n = 1008) of resident doctors and 65.7% ( n = 889) of medical students. Notably, 56.8% ( n = 573) and 79.3% ( n = 705) of resident doctors and medical students affected by burnout, respectively, were unaware of available support services. Relocation stress (85.1% of residents and 62.6% of students), financial stress (53.9% of residents and 57.8% of students) and poor work–life balance (32.1% of residents and 20.7% of students) were commonly reported. The annual economic burden of absenteeism related to burnout among resident doctors was estimated to reach £251.5 million. Conclusions Burnout among UK resident doctors and medical students may reflect a combination of underlying factors, including financial strain, relocation stress and difficulties with work–life balance. These findings underscore the need for targeted interventions that support well-being and address financial and relocation challenges, which may, in turn, help prevent burnout and improve retention within the medical workforce. Reducing burnout and associated stressors may also help lessen its economic impact, allowing resources to be redirected towards patient care.

Abstract Aims We aimed to compare procedural, echocardiographic, and clinical outcomes within the first year after transcatheter aortic valve implantation (TAVI) among inhospital and subacute patients to elective patients in the COMPARE-TAVI 1 cohort. Methods and results Among 1030 patients treated with TAVI, 927 (90%) had an elective procedure, 63 (6.1%) had a subacute procedure, and 40 (3.9%) had an inhospital procedure. Procedural characteristics and outcomes were comparable. Among elective, subacute, and inhospital patients, mortality at 30 days was 15 (1.6%), 0 (0%), and 0 (0%) and at 1 year 54 (5.8%), 4 (6.3%), and 2 (5.0%). Stroke at 30 days was 28 (3.0%), 0 (0%), and 3 (7.5%) and at 1 year 42 (4.5%), 5 (7.9%), and 3 (7.5%). New pacemaker at 30 days was 119 (14%), 12 (21%), and 6 (17%) and at 1 year 130 (16%), 14 (25%), and 7 (19%). Elective, subacute, and inhospital patients with LVEF ≤40%, improved their LVEF from 33% (28–37), 33% (27–37), and 28% (22–33) before TAVI to 46% (35–60), 43% (33–52), and 42% (33–55) at 30-days, and 54% (45–62), 53% (43–61), and 49% (39–63) at 1-year. Differences between the groups were not statistically significant. Conclusion After transfemoral TAVI with balloon-expandable valves in COMPARE-TAVI 1, procedural, echocardiographic, and clinical outcomes were comparable among elective, subacute, and inhospital patients within the first year.

Introduction Geriatric distal femur fractures are associated with mortality rates exceeding 20%, comparable to hip fractures. Traditional single implant fixation often requires weight-bearing restrictions that delay recovery. This pilot study aims to assess the feasibility of conducting a multicentre randomised controlled trial comparing dual vs single implant fixation for geriatric distal femur fractures. Methods and analysis This multicentre, prospective, randomised controlled pilot trial will enrol 80 participants aged 60 years or older with displaced distal femur fractures at five US level 1 trauma centres. Patients will be randomly allocated 1:1 to receive either single implant (lateral plate or retrograde nail) or dual implant (nail-plate or dual plate) fixation. All patients will be permitted immediate weight-bearing. The primary outcome is feasibility assessed through enrolment rate (80 patients in 12 months), protocol adherence (≥90%) and follow-up retention (≥85% at 12 months). Secondary outcomes include post-surgical mobility (AM-PAC, TUG), patient-reported outcomes (PROMIS-PF, PROMIS-29), mortality (90-day and 1 year) and complication rates. Analyses will be on an intention-to-treat basis. Ethics and dissemination The protocol was approved by the University of Utah Institutional Review Board (IRB_00149119) and IRBs at all participating centres. Written informed consent will be obtained from participants or legally authorised representatives. Findings will be disseminated through peer-reviewed publications and conference presentations. Trial registration number NCT05292313 .

ABSTRACT Background Polypharmacy is common in autoimmune rheumatic diseases (ARDs) and increases adverse drug events (ADEs), but comparative evidence across diseases is limited. We aimed to quantify ADE burden and identify medications associated with ADE risk across six ARDs, and to examine shared and disease-specific patterns across diseases. Methods We conducted a retrospective cohort study at a tertiary medical center (2010–2024). Adults with ankylosing spondylitis (AS), psoriatic arthritis (PsA), rheumatoid arthritis (RA), Sjögren’s disease (SjD), systemic lupus erythematosus (SLE), or systemic sclerosis (SSc) were identified using diagnostic codes. ADEs were ascertained using validated case definitions. Medications were mapped to Anatomical Therapeutic Chemical classes; active exposure was defined within 30 days before the index date. Polypharmacy was defined as ≥5 concurrent medications (minor 5–10; major >10). Within each ARD, nested case–control analyses matched on encounter type (1:4) were performed, and adjusted odds ratios (aORs) were estimated using conditional logistic regression. Findings Among 10,578 patients, 3,154 (29.8%) experienced at least one ADE. ADE burden varied across diseases, with the highest prevalence observed in SSc (35.9%). Polypharmacy was common (57.3% minor, 39.4% major) and medication burden was consistently higher in ADE cases across encounter types (eg, SLE outpatient median 12 vs 6; inpatient 20 vs 10; emergency 17 vs 8). Across ARDs, the strongest associations with ADEs were observed for supportive and symptom-directed therapies (acid suppressors, pain adjuncts, and sedative-hypnotic/psychotropic medications), whereas conventional disease-modifying antirheumatic drugs (DMARDs) showed weaker associations. Disease-specific signatures included gastrointestinal agents in SSc (metoclopramide aOR 12.32), antibiotics and respiratory agents in AS (ciprofloxacin aOR 13.71, fluticasone aOR 8.88). Interpretation ADEs affect nearly one-third of ARD patients and increase with medication burden. Risk concentrates in supportive and symptom-directed therapies rather than DMARDs, with both shared and disease-specific patterns. Optimizing prescribing, particularly for pain management and corticosteroid use, can reduce medication-related harm.

Emerging evidence suggests that systemic physiological factors may influence plasma biomarker concentrations of Alzheimer’s disease (AD) and related neurodegenerative processes, potentially affecting their specificity for central nervous system pathology. This study examined the relationship of demographic factors and medical comorbidities with plasma biomarkers of AD and neurodegeneration in a community-dwelling cohort of Black/African American (B/AA) older adults (N = 141). Participants underwent plasma assessment of phosphorylated tau at threonine 217 (p-Tau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Results showed associations between plasma p-Tau217 and amyloid PET positivity, and significant intercorrelations among p-Tau217, GFAP, and NfL. Stepwise regression models incorporated demographics, amyloid PET status, and laboratory measures of renal, metabolic, and lipid function as predictors for each biomarker. p-Tau217 was primarily predicted by amyloid PET and renal function; GFAP by age and sex; and NfL by renal function, age, and sex. Findings indicate plasma biomarker concentrations in B/AA older adults reflect both central AD-related pathology and systemic physiological factors, particularly renal function, and demographic influences. Results underscore the importance of accounting for comorbid medical conditions and demographic characteristics when interpreting blood-based biomarkers and highlight the need for comprehensive medical phenotyping to improve diagnostic specificity and clinical utility.

Abstract Heart involvement in eosinophilia represents a rare entity with substantial cardiac morbidity and mortality. This disorder is often overlooked and diagnosed in its advanced stages. In a subgroup of patients with eosinophilic heart disease, peripheral blood eosinophilia may not be present, and the biopsy of the affected organ or tissue is essential to establish the correct diagnosis. In this clinical consensus statement, an expert consensus group reviews the epidemiology, aetiology, diagnosis, and management of eosinophilic heart disease. The aim of the document is to increase awareness about the cardiac involvement associated with eosinophilia in order to diagnose the disease in early phases and prevent deleterious heart damage and adverse outcomes of affected individuals. The spectrum of physiological and immunological derangements requires a multidisciplinary approach for optimal management of affected individuals.

The sharp dose gradients that underpin the dosimetric advantage of particle therapy over photon therapy can be undermined by the interplay effects due to intra-fraction motion in modern pencil beam scanning systems. Fluoroscopy-Guided Particle Therapy (FGPT) offers a promising path to improved motion management through real-time tracking of tumors or surrogate signals. The advent of flat-panel detector (FPD)-based technology has enabled tighter integration of fluoroscopy/fluorography into treatment units and accelerated clinical adoption and research, with commercial systems such as Hitachi’s Real-time Gated Particle Therapy (RGPT) now available. However, the need for implanted fiducial markers, with the associated invasiveness and risk of complications, limits the utility of RGPT to a few anatomic sites in selected patients. The full potential of FGPT, therefore, depends on reliable marker-less tumor tracking, which remains challenging because soft-tissue targets are obscured by overlapping anatomy along the X-ray path, leading to reduced reliability of traditional image-registration algorithms in the projection domain. Recent advances in deep learning and AI-driven image registration have renewed hope for overcoming these barriers, enabling real-time marker-less tracking for particle therapy. This review outlines the evolution of fluoroscopy technology from image intensifier (II) to FPD-based systems, summarizes historical and recent vendor-supported FGPT strategies, and surveys emerging AI-based algorithms in the literature. A general review of machine learning-based image registration is provided, challenges in generalizability and interpretability are highlighted, and potential paths toward reliable, clinically deployable FGPT are discussed.

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information ( Appendix 1 and Appendix 2 ). Updates are published regularly and can be found on the ASCO Publications website .

Foundation models are reshaping computational pathology by enabling scalable task-agnostic representations of histopathological whole-slide images (WSIs). Unlike earlier task-specific deep learning systems, pathology foundation models (PFMs) leverage massive whole-slide image repositories and self-supervised Vision Transformer architectures to achieve broad generalization and few-shot adaptability. Their evolution reflects a shift from weakly supervised approaches such as Clustering-Constrained Attention Multiple Instance Learning (CLAM) and hierarchical architectures such as Hierarchical Image Pyramid Transformer (HIPT) to large-scale efforts including foundation models, UNI, Virchow, Phikon, CONtrastive learning from Captions for Histopathology (CONCH), GigaPath, H-Optimus, Transformer-Based Pathology Image and Text Alignment Network (TITAN), and the Mayo Clinic Atlas. These models demonstrate impressive performance across diagnostic and prognostic benchmarks while also opening pathways for multimodal integration with genomics and clinical data. Yet significant barriers remain including inconsistent generalization across institutions, interpretability lagging behind clinical needs, and slow integration into routine laboratory workflows. Certain domains of anatomic pathology such as cytopathology, transplant pathology, frozen sections, and rare tumor subtypes remain particularly resistant to current models. Here, we review the development of PFMs, critically evaluate their strengths and limitations, and outline priorities for their safe and effective clinical translation. We argue that the next phase of PFM development will depend on rigorous benchmarking, pathologist-in-the-loop deployment, and multimodal fusion ensuring these models evolve from research tools into clinically robust systems.