Development of a Machine Learning Algorithm to Identify Cauda Equina Compression on MRI Scans

Objective: Cauda Equina Syndrome (CES) poses significant neurological risks if untreated. Diagnosis relies on clinical and radiological features. As the symptoms are often non specific and common, the diagnosis is usually made after a MRI scan. A huge number of MRI scans are done to exclude CES but nearly 80% of them will not have cauda equina syndrome. This study aimed to develop and validate a machine learning model for automated CES detection from MRI scans to enable faster triage of patients presenting with CES like clinical features.Methods: MRI scans from suspected CES patients (2017-2022) were collected and categorized into normal scans/disc protrusion (0%-50% canal stenosis (CS)) and cauda equina compression (CEC, >50% CS). A convolutional neural network was developed and tested on a total of 715 images (80:20 split) Gradient descent heatmaps were generated to highlight regions crucial for classification.Results: The model achieved an accuracy of 0.950 (0.921-0.971), a sensitivity of 0.969 (0.941-0.987), a specificity of 0.859 (0.742-0.937), a positive predictive value of 0.969 (0.944-0.984) and an area under the curve of 0.915 (0.865-0.958). Gradient descent heatmaps demonstrated accurate identification of any clinically relevant disc herniation into the spinal canal.Conclusions: This study pilots a deep learning approach for predicting CEC presence, promising improved healthcare quality and timely CES management. As referrals rise, this tool can act as a fast triage system which can lead to prompt management of CES in environments where resources for radiological interpretation of mri scans is limited

Omnipod5 Real-World Data from the First Pediatric Users’ Universal Coverage Under the UK National Health Service

Background: Hybrid closed-loop (HCL) systems combine continuous glucose monitoring (CGM) with insulin pumps to automate insulin delivery through specific algorithms and user input. This real-world study aimed to evaluate the effectiveness of the Omnipod5 HCL system on HbA1c, time-in-range (TIR), hypoglycemia frequency, and sensor glucose variability over 3 and 6 months in children and young people with type 1 diabetes at two National Health Service (NHS)-funded pediatric diabetes centers in North West England. Methods: Children younger than 18 years in two teaching hospital-based diabetes centers were started on Omnipod5 between August 2023 and January 2024. Sensor glucose metrics and HbA1c were collected within 3 months before Omnipod5 initiation and compared at 3 and 6 months postinitiation. Metrics included % TIR (sensor glucose 70-180 mg/dL), % time above range (TAR) (sensor glucose &gt;180 mg/dL and &gt;250 mg/dL), and % time below range (TBR) (sensor glucose &lt;70 mg/dL mmol/L and &lt;54 mg/dL), with variability assessed by coefficient of variation (CV) and standard deviation (SD). Results: A total of 144 children were included, with 46% males and a mean age of 7.1 years (SD 4.3). The cohort was predominantly White (80%), with diabetes duration averaging 4.4 years (SD 3.9). Before Omnipod5, 54% used multiple daily injections, 41% a nonintegrated pump, and 5% another HCL system. At 3 and 6 months postinitiation, there were significant improvements in HbA1c from 7.7% (60.2 mmol/mol) to 7.1% (54.4 mmol/mol) at 3 months and 7.2% (55.2 mmol/mol) at 6 months. TIR improved from 53.3% at baseline to 67.4% at 3 months and 68.8% at 6 months), and reductions in TAR, TBR, and CV were also observed. Conclusions: These findings highlight the Omnipod5 system’s safety and effectiveness in improving glycemic control for children and young people (CYP) with type 1 diabetes in a real-world NHS setting. Further research is needed to explore the long-term benefits and cost-effectiveness of this tubeless HCL system in routine clinical care.</p

EPOCH Tutorials - Reading Resistance: A Practical Guide to Working with The National Archives Ancient Petitions Collection (SC 8)

Intrigued by Issue 20's article on the politics of Plantagenet petitions? Read on for a practical guide to working with petitions in your own research

Connecting generations: the ESPNIC mentorship bridge

Mentorship is crucial for career development in healthcare. This report describes the ESPNIC Mentorship Program, established in 2021, which pairs junior healthcare professionals with senior mentors to support career goals. Over four years, 83 pairs were formed, including diverse participants from various roles and countries. Demographic information and feedback forms from mentors and mentees were collected systematically via online surveys at 12-month intervals at the start and end of the program. These data were thematically evaluated to find important areas of satisfaction and recommendations for development. Participants from low and middle income countries were enrolled in the program as well representing 26% of the mentees (22/83) and 10% of mentors (7/67). Women represented 50% (34/67) of mentors; and 71% (59/83) of mentees. Key areas of collaboration included career development, research, international networking, and clinical support. The program emphasised a voluntary and non-judgemental approach, fostering a positive experience for both mentors and mentees, and Feedback from both mentors and mentees so far has been very positive. The ESPNIC Mentorship Program serves as a model for other professional societies seeking to enhance member support and foster career advancement in paediatric and neonatal critical care

Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers

This article explores the application of Retrieval-Augmented Generation (RAG) to enhance the creation of knowledge assets and develop actionable insights from complex datasets. It begins by contextualising the limitations of large language models (LLMs), notably their knowledge cut-offs and hallucination tendencies, and it will present RAG as a promising solution that integrates external knowledge retrieval to improve factual accuracy and relevance. This study reviews current RAG architectures, including naïve and advanced models, emphasising techniques such as optimised indexing, query refinement, metadata utilisation, and the incorporation of autonomous AI agents in agentic RAG systems. Methodologies for effective data preprocessing, semantic-aware chunking, and retrieval strategies—such as multihop retrieval and reranking—are also discussed to address challenges such as irrelevant retrieval and semantic fragmentation. This work further examines embedding models, notably the use of state-of-the-art vector representations, to facilitate precise similarity searches within knowledge bases. A case study demonstrates the deployment of an RAG pipeline for analysing multisheet datasets, highlighting challenges in data structuring, prompt engineering, and ensuring output consistency

Wheat3D PartNet: Annotated Dataset for 3D Wheat Part Segmentation

High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and their growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust datasets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective techniques. To address this, we propose Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were subject to drought and watered conditions, acquired from multiple viewpoints and represent different plant structures at different growth stages. Wheat3D PartNet samples are manually labelled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems). It is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection—facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset's utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest

The modulatory role of extrinsic motivation in the relationship between fear of failure and student engagement

Teachers often employ various techniques to motivate and engage students. They may choose to use positive fear appeals as a motivational tactic to stimulate fear that will result in students making greater efforts to avoid failure or they may employ extrinsic incentives to engage students. This study examined the modulatory role of extrinsic motivation, as a differentiated construct, in the relationship between fear of failure and student engagement. Data were collected using self-reported instruments and analysed using moderation and mediation analyses. Extending the motivation literature, this study, sheds new light on the positivemodulatory role of extrinsic motivation regulations in the relationship between fear of failure and student engagement. Contributions to practice are implied; there is a need for educators to understand the role of self-imposed and self-endorsed behaviours in influencing engagement among students with high and low fear of failure. Comprehending the complexity of the learning environment in light of the complex nature of human behaviours is considered essential to improving teaching and learning

The Burden of Treatment: Experiences of Patients Who Have Undergone Radiotherapy and Proton Beam Therapy

Background/Objectives: The physical and psychosocial impacts of cancer treatment can be distressing and profound for many patients, but little is known about the specific impacts of undergoing radiotherapy and proton beam therapy (PBT). This study explores the hidden burdens of treatment and aims to identify the gaps in our current understanding of patients’ experience when attending a large radiotherapy and PBT service. Methods: A qualitative study using semi-structured interviews was conducted with patients undergoing treatment. A purposive sample of participants were recruited, reflecting the main indications for radiotherapy and PBT. Semi-structured interviews were conducted between August 2023 and January 2024 either in person, virtually, or by telephone. Data were analysed using Framework Analysis. Results: In total, 20 patients were interviewed. Five themes were identified: informational needs, emotional wellbeing, logistical concerns, physical impacts, and interpersonal impacts. Patients reported additional financial burdens such as transport and staying away from home, difficulty carrying out normal responsibilities, caregiver burden, and increased anxiety. Many patients reported the post-treatment drop in healthcare interaction, which resulted in distress and isolation, difficult. Conclusions: This study indicates that there are many burdens of radiotherapy and PBT outside of the physical symptoms and side-effects of cancer treatment. Tailored support is needed to address treatment-specific concerns within the radiotherapy and PBT service, but this study also suggests that supportive interventions developed for broader cancer populations may be helpful for this patient cohort

Poetic Environmental Activism and Education:Thoreau and Shepherd for Times of Ecological Crises

Activism is all around us, and for many causes, has never been more needed. Perhaps this is particularly true of environmental activism as we face the stark reality of the global crises affecting our natural world, and come to increasingly experience the climate emergency. Amid the cacophony of activists’ voices on the environment, we offer a perspective on a quieter – and yet no less profound and important – form of activism that we find in the literary works of the 19th century American philosopher and essayist, Henry David Thoreau, and the 20th century Scottish modernist novelist and poet, Anna [Nan] Shepherd. In this new volume which systematically brings together the work of these writers for the first time, we propose that their work constitutes a form of poetic environmental activism. Through lives spent in nature (walking the forests around Walden Pond in Concord, Massachusetts, and in the Cairngorm Mountains in Scotland), both writers document the natural world in its beauty, complexity, and richness. This poetic documentation, characterised by advocating, and accounting for, the world around them, calls our attention back to the natural world on which we so rely, but from which we have become increasingly disconnected. Poetic environmental activism is a different form of rallying call: one that is replete with rich possibilities for education. It calls for our learning care-ful attitudes towards our environment; it re-engages and re-connects us with the urgency of action needed in response to changes to our natural world, and it offers a new way of reading Thoreau and Shepherd’s work as exemplary texts for our time