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Patient education following vertebral fragility fracture: a scoping review.
UNLABELLED: Vertebral fragility fracture (VFF) incidence is rising with significant associated patient and health service burden. Patient education is core to effective VFF management and thus the focus of this robust literature review. Eight studies met review inclusion criteria with the limited evidence and inconsistent approach to education post VFF illustrated. PURPOSE: The incidence of vertebral fragility fracture is rising, associated with osteoporosis among an ageing global population. Most VFFs are managed conservatively with patient education, a core element of conservative management. This review aims to identify and synthesise the available literature regarding healthcare professional (HCP)-led patient education post VFF. METHODS: This review was registered on OSF [25] and conducted in accordance with the Joanna Briggs Institute methodology [21] for scoping reviews and guided by the Arksey and O'Malley Framework [22], using five key stages: (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) charting the data and (5) charting, collecting and summarising the data. Six databases (Pubmed, ERIC, Embase, Cinahl, APA PsychINFO and Cochrane) were searched for papers published (in English) from January 2008 to November 2023, using a clearly defined search strategy. Predefined inclusion/exclusion criteria were used for screening papers using Covidence software [26] and a minimum of two independent reviewers. Data were extracted from full-text articles which met the inclusion criteria, with narrative synthesis of findings. Reporting of results adhered to the PRISMA-Scoping review checklist. RESULTS: Title and abstract screening was conducted for the 7177 retrieved studies. Of the 34 papers identified for full text review, 8 studies (from Canada, Norway, Sweden, China and the UK) met the scoping review inclusion criteria. These included four randomised controlled trials, one pilot RCT, one non RCT one retrospective analysis of data from patients with fragility fractures and one pre-post interventional study. Where specified in the papers, education providers were a mix of healthcare professionals (Physiotherapists, Nurses, Doctors, Dietitians, Occupational Therapists). Education was delivered in a variety of settings using verbal, written and visual communication media. The most common education topics were exercise, osteoporosis, nutrition and falls management. A diversity of outcome measures captured patient knowledge, quality of life, falls efficacy, physical function, balance and pain. CONCLUSION: Findings of this review demonstrate the limited evidence and an inconsistent approach to education post VFF in terms of education topics, mode of delivery and outcome measures. A range of HCP disciplines deliver education. Research is critically needed to inform the development and delivery of effective, evidence-based education interventions for VFF management.
Inhomogeneous Response of Articular Cartilage: A Three-Dimensional Multiphasic Heterogeneous Study.
Articular cartilage exhibits complex mechano-electrochemical behaviour due to its anisotropy, inhomogeneity and material non-linearity. In this work, the thickness and radial dependence of cartilage properties are incorporated into a 3D mechano-electrochemical model to explore the relevance of heterogeneity in the behaviour of the tissue. The model considers four essential phenomena: (i) osmotic pressure, (ii) convective and diffusive processes, (iii) chemical expansion and (iv) three-dimensional through-the-thickness heterogeneity of the tissue. The need to consider heterogeneity in computational simulations of cartilage behaviour and in manufacturing biomaterials mimicking this tissue is discussed. To this end, healthy tibial plateaus from pigs were mechanically and biochemically tested in-vitro. Heterogeneous properties were included in the mechano-electrochemical computational model to simulate tissue swelling. The simulation results demonstrated that swelling of the heterogeneous samples was significantly lower than swelling under homogeneous and isotropic conditions. Furthermore, there was a significant reduction in the flux of water and ions in the former samples. In conclusion, the computational model presented here can be considered as a valuable tool for predicting how the variation of cartilage properties affects its behaviour, opening up possibilities for exploring the requirements of cartilage-mimicking biomaterials for tissue engineering. Besides, the model also allows the establishment of behavioural patterns of swelling and of water and ion fluxes in articular cartilage.
Clinical outcomes following treatment of extracapsular hip fractures with long versus short cephalomedullary nails
Aims: The use of cephalomedullary nails for the fixation of extracapsular hip fractures has risen in recent years but high-quality evidence to guide surgical decision-making between long and short cephalomedullary nails is lacking. The aim of this study was to compare the quality of life (QoL) and the risks of mortality and complications in patients treated with long and short cephalomedullary nails.Methods: The World Hip Trauma Evaluation (WHiTE) study was a multi-centre, prospective cohort study that collected data from patients ≥ 60 years who received operative treatment for their hip fracture. Patients were followed up for 120 days after surgery. The primary variable of interest was cephalomedullary nail type (long or short). The primary outcome was healthrelated QoL (EQ-5D-5L). The secondary outcomes were mortality and complications. Linear and Cox proportional hazards regression models were fitted to assess the relationship between cephalomedullary nail type, EQ-5D-5L, mortality, and complications, respectively.Results: Among 2,640 patients with an extracapsular hip fracture, 2,014 patients were treated with a long cephalomedullary nail and 626 patients with a short cephalomedullary nail. The adjusted mean difference in EQ-5D-5L in patients treated with a long and short cephalomedullary nail was 0.02 (95% CI: -0.01 to 0.05, p = 0.144). The adjusted hazard ratios associated with treatment with a short cephalomedullary nail compared to a long cephalomedullary nail for mortality was 0.96 (95% CI: 0.70 to 1.30, p = 0.772); re-operation 0.97 (95% CI: 0.54 to 1.76, p = 0.919); fixation failure 1.20 (95% CI: 0.47 to 3.06, p = 0.710); peri-implant fracture 0.97 (95% CI: 0.30 to 3.17, p = 0.959); surgical site infection 1.06 (95% CI: 0.60 to 1.86, p = 0.838); and blood transfusion 0.52 (95% CI: 0.37 to 0.72, p < 0.001).Conclusion: Patients treated with long versus short cephalomedullary nails for an extracapsular hip fracture experienced a similar recovery in quality of life. The implants had similar risk profiles in terms of mortality and surgical complications although the risk of blood transfusion was higher after treatment with a long cephalomedullary nail. Surgeons may choose between long and short cephalomedullary nail according to the fracture type and patient factors in the knowledge that both implants provide similar outcomes.
Ipilimumab with temozolomide vs. temozolomide alone after surgery and chemoradiotherapy in recently diagnosed glioblastoma: a randomized phase II clinical trial.
BACKGROUND: Glioblastoma confers a bleak prognosis, with median survival of less than a year. This trial evaluated whether addition of the CTLA-4 immune checkpoint inhibitor ipilimumab to standard therapy improves survival in patients with recently diagnosed glioblastoma. METHODS: Ipi-Glio was a stratified randomized, open-label, multicenter, academic phase II study. Patients with recently diagnosed de novo glioblastoma following completion of chemoradiotherapy were randomized 2:1 to ipilimumab + temozolomide (Arm A) vs temozolomide alone (Arm B), stratified to extent of surgery and MGMT promotor methylation. Primary endpoint was overall survival. Secondary endpoints included progression-free survival at 18 months, overall survival at 3 years, and toxicity (≥Grade 3). RESULTS: One hundred nineteen patients were randomized (79 to Arm A, 40 to Arm B). Patient characteristics (Arm A vs B): median age 57 vs 49 years; male sex 70 vs 65%, gross total resection 61 vs 60%, tumor MGMT promotor methylation 39 vs 40%. Median overall survival was 18 months (60% CI 16.0, 23.9) in Arm A vs 23.0 months (17.3, 26.4) in Arm B (adjusted HR 1.09, 60% CI 0.86,1.38, one-sided P = .62; logrank P = .75). Progression-Free Survival: 10.8 vs 12.5 months (Arm A vs B) (adjusted HR 1.34, 1.06-1.68, one-sided P = .86;logrank P = .42). Grade 3 or above adverse events: 53% Arm A vs 43% Arm B (P = .27). CONCLUSIONS: No benefit was observed with the addition of ipilimumab to temozolomide in patients with recently diagnosed glioblastoma following chemoradiotherapy. This study does not support further investigation of this regimen in this setting. TRIAL REGISTRATION: ISRCTN84434175 (www.isrctn.com/ISRCTN84434175).
Magnetic Resonance Imaging Derived Cartilage Morphological Changes and their Correlation with Patient-Reported Outcome Measures Following Knee Joint Distraction for Osteoarthritis: A 12-Month Cohort Study.
AimsKnee osteoarthritis (OA) is a significant source of morbidity and socioeconomic burden, exacerbated by aging populations and rising body mass index. Total Knee Replacement (TKR) is effective but may result in dissatisfaction or revision, particularly in young patients. Knee Joint Distraction (KJD) offers a joint-preserving alternative that may delay or avoid replacement. This study assessed cartilage morphology changes using magnetic resonance imaging (MRI) of patients up to 1-year post-KJD in patients from a randomized controlled trial (RCT). The primary aim was to evaluate cartilage volumes at 12 months post-KJD. Secondary aims were to evaluate additional MRI parameters for cartilage morphology and compare the MRI parameters with Patient-Reported Outcome Measure (PROM) scores at 3 and 12 months.MethodsA subset of participants from an RCT comparing TKR and KJD were analyzed. The MRI and PROMs, including Knee Injury & Osteoarthritis Outcomes Score (KOOS), Oxford Knee Score (OKS), and pain visual analogue scale (VAS), were collected at baseline, 3 months, and 12 months postintervention. Cartilage segmentation using commercial software and grading using the MRI Osteoarthritis Knee Score (MOAKS) were performed.ResultsTen patients were included. Increases in mean cartilage volume were observed in all regions except the trochlear at both follow-ups. Mean cartilage thickness increased in all areas except the lateral tibia. Mean denuded bone area decreased in all regions at 12 months and in the lateral femur at 3 months. Baseline cartilage status was predictive of treatment response.ConclusionKJD led to improvements in cartilage morphology up to 12 months, suggesting its potential as a joint-preserving strategy for knee OA. Further long-term studies are needed to confirm benefits and understand mechanisms.
Sociodemographic factors, biomarkers and comorbidities associated with post-acute COVID-19 sequelae in UK Biobank.
Long-term sequelae of COVID-19 remain critical public health concerns, with limited therapeutic options available. We conducted two case-control studies among COVID-19 infected individuals in the UK Biobank to explore the association of sociodemographic factors, clinical biomarkers, and comorbidities with the risk of two key phenotypes: Long COVID (LC, defined by patient self-report symptoms) and post-acute complications of SARS-CoV-2 infection (PACS, defined by clinical diagnosis), separately. Our study included 8,668 participants in the LC cohort (32% classified as cases) and 108,407 in the PACS cohort (with 2% being cases). Findings showed that age and sex were associated with both LC and PACS but in opposite directions. Additionally, obesity, socioeconomic deprivation, elevated C-reactive protein, triglyceride, vitamin D, HbA1c, cystatin C, urate, and alanine aminotransferase, and decreased HDL cholesterol and IGF-1, as well as CKD and COPD, were associated with LC. Most of these factors were also significant for PACS, except for alanine aminotransferase and vitamin D. These findings have potential mechanistic implications for the distinction between LC and PACS and can guide clinical implementation of identifying high-risk groups for targeted vaccination or other public health mitigation strategies.
Data-Driven Approach to Assess and Identify Gaps in Healthcare Set up in South Asia
Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages [11]. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.
Deep Learning-Based Task Offloading for Efficient and Reliable Computation in High-Mobility Vehicular Networks
In vehicular networks, resource-constrained vehicles often face challenges in executing computationally intensive tasks due to limited local resources. Task offloading to nearby vehicles with sufficient resources provides an effective solution. However, in high-mobility scenarios, selecting the most appropriate vehicle for task offloading becomes a challenging and time-consuming process, leading to increased delays and degraded network performance. This paper proposes a novel deep learning-based task offloading technique to address this issue. The proposed approach operates in two stages. First, a deep learning model classifies nearby vehicles into eligible and unqualified nodes based on their ability to meet task requirements. Second, from the pool of eligible nodes, vehicles are ranked according to their credibility scores. Credibility scores are dynamically updated based on task completion within specified deadlines. By prioritizing vehicles with high credibility scores, the proposed technique ensures efficient and reliable task execution. Experimental results demonstrate that the proposed approach significantly improves the task execution success rate, reduces task offloading delays, and enhances the overall performance of vehicular networks.
Kiln-Net: A Gated Neural Network for Detection of Brick Kilns in South Asia
The availability of high-resolution satellite imagery has enabled several new applications such as identification of brick kilns for the elimination of modern-day slavery. This requires automated analysis of approximately 1 551 997 text{km}^2 area within the 'Brick-Kiln-Belt' of South Asia. Although modern machine learning techniques have achieved high accuracy for a wide variety of applications, problems involving large-scale analysis using high-resolution satellite imagery requires both accuracy as well as computational efficiency. We propose a coarse-to-fine strategy consisting of an inexpensive classifier and a detector, which work in tandem to achieve high accuracy at low computational cost. More specifically, we propose a two-stage gated neural network architecture called Kiln-Net. At the first stage, imagery is classified using the ResNet-152 model which filters out over text{99}% of irrelevant data. At the second stage, a YOLOv3-based object detector is applied to find the precise location of each brick kiln in the candidate regions. The dataset, named Asia14, consisting of 14,000 Digital Globe RGB images and 14 categories is also developed to train the proposed kiln-net architecture. Our proposed network architecture is evaluated on approximately text{3{,}300} km^2 region (337,723 image patches) from 14 different cities in five different countries of South Asia. It outperforms state-of-the-art methods employed for the recognition of brick kilns and achieved an accuracy of text{99.96}% and average F1 score of 0.91. To the best of our knowledge, it is also 20,x faster than existing methods.
The impact of implementing the women's reproductive rights agenda on climate change.
The 1994 International Conference on Population and Development (ICPD) established sexual and reproductive health and rights (SRHR) as foundational to sustainable development. Thirty years later, advancing women's reproductive rights (WRR), encompassing agency, decision-making autonomy, and universal access to family planning-remains critical not only for health and gender equity but also for mitigating environmental degradation. By reducing unintended pregnancies and empowering women to align childbearing with personal and ecological capacity, WRR alleviates ecological stressors such as deforestation while enhancing health resilience in climate-vulnerable communities. Yet, despite well-documented linkages between population dynamics and environmental change, contemporary climate policies and funding mechanisms persistently exclude WRR. This oversight undermines the potential of reproductive justice to enhance climate resilience. Additionally, claims that integrating WRR into climate agendas covertly promotes population control or represses women in low- and middle-income countries are fundamentally misleading. Crucially, research is needed to quantify the specific environmental impacts of WRR, underscoring the urgent need for robust global models to predict and validate these co-benefits. Strengthening this evidence base is imperative to inform policies that integrate WRR indicators into climate financing frameworks, ensuring gender-responsive programming. Bridging this gap requires interdisciplinary collaboration to develop metrics that capture WRR's role in reducing resource consumption and enhancing adaptive capacity. Embedding WRR within climate agendas would harmonize reproductive justice with environmental action, unlocking synergies between gender equity, health resilience, and sustainability. Fulfilling the ICPD's vision demands centering WRR in global climate strategies, thereby advancing a just and livable future for all.
Is Fine-Tuning Useful in EHR-Based Prediction Models? a Use Case on Mortality Prediction with Longitudinal Data from Spanish (SIDIAP) and UK (CPRD) Populations Aged Over 65 Years
Transfer learning enables the reuse of models trained on large datasets, reducing data collection, computation time, and costs. While widely used in computer vision, its application to models based on electronic health records (EHRs) remains limited. This study evaluates whether fine-tuning an EHR-based model from one country to another outperforms training a model from scratch. EHR from the SIDIAP (Spain) and CPRD (UK) databases were used, defining a cohort in each country of individuals aged 65+ followed between 2010 and 2019. A prediction model was trained and validated internally for each country to predict 1-year mortality, then externally validated and fine-tuned with the other country's population (recalibrated model). The models were based on ARIADNEhr, a previously validated architecture. Performance metrics, decision curve analysis, and attention maps were compared. Participants included 1,456,052 from SIDIAP and 1,507,736 from CPRD, with similar demographics. Performance on the external cohort varied between $\mathbf{- 1 0. 9 \%}$ and $\mathbf{+ 3 9. 5 \%}$. Fine-tuning consistently improved external performance (1.8 % − 15.5 %), enhanced model calibration and clinical utility, and maintained key contributing variables. However, the fine-tuned models did not reach the performance of the country-specific models, showing a performance drop between 14 % and 20 %. Fine-tuning may be useful in other fields but still insufficient for tabular EHR-based prediction models in health applications.