Contested cultures of care: research with and for the plus one community on the plus one experience-evaluation report

This study was funded by the Derbyshire and Nottinghamshire Collaborative Outreach Progamme (DANCOP). It focusses mainly on a series of holiday workshops run by the Derby Cultural Education Partnership (CEP) for young people with direct experiences of the care system. The findings reported here result from a variety of qualitative methods and represent the shared understanding of Plus One between researchers from the University of Derby, arts practitioners at Derby Theatre and the other CEP partners and the young people who participate in the programme

Priority setting for oncology in South Africa using a burden of disease approach

Objectives To forecast the provincial supply of oncologists in South Africa through 2030 using a health need–based approach grounded in disability-adjusted life years (DALYs), and to identify shortfalls under scenarios aimed at reducing human resources for health (HRH) inequities as highlighted in Disease Control Priorities, Volume 3 (DCP-3). Study design A retrospective forecasting study employing DALY-driven demand projections for oncology services in each of South Africa's nine provinces, with scenario analyses evaluating horizontal equity in HRH distribution. Methods Age-standardized provincial DALYs for cancer were obtained from the Institute for Health Metrics and Evaluation Global Burden of Disease (IHME GBD) estimates via the Global Health Data Exchange (GHDx). Mid-year population estimates for 2018 were sourced from Statistics South Africa. Using these metrics, we calculated DALY load per oncologist and projected oncologist requirements for 2020, 2025, and 2030. Results Under the best guess scenario, South Africa faces a shortfall of 47 oncologists in 2020, increasing to 97 by 2025 and 148 by 2030. The optimistic scenario yields national deficits of 77 (2020), 126 (2025), and 175 (2030). In the aspirational scenario, shortfalls climb to 138 (2020), 184 (2025), and 230 (2030). Conclusions The Workforce Projection Model offers a replicable framework for low- and middle-income countries to assess oncology workforce needs, optimize HRH allocation, and plan capacity development to enhance equitable access to cancer care

Evaluating machine learning models for cardiovascular risk prediction: A Shapley Additive Explanations-based approach with statistical testing

Cardiovascular disease (CVD) remains the leading global cause of mortality, underscoring the need for accurate and interpretable prediction models to facilitate early diagnosis. Existing machine learning (ML) approaches often face challenges balancing predictive performance with clinical interpretability, limiting their adoption. This study introduces a structured evaluation framework combining A/B testing with statistical hypothesis validation to rigorously compare ML models for CVD risk prediction. Utilizing a dataset of 1,001 patient records, models including logistic regression, random forest (RF), artificial neural networks, and extreme gradient boosting (XGBoost) were trained and evaluated. Synthetic Minority Oversampling Technique was applied to address class imbalance, while Shapley Additive Explanations (SHAP) provided insights into feature contributions and guided the development of reduced-feature models. Results indicate that RF achieved the highest accuracy (98.5%) and area under the receiver operating characteristic curve (0.9991), whereas XGBoost coupled with SHAP enabled effective feature selection with minimal loss in predictive power. A/B testing demonstrated the trade-offs between model complexity and interpretability, while statistical testing confirmed the significance of performance differences. These findings suggest that interpretable, reduced-feature models may be viable for deployment in resource-limited clinical settings, advancing the integration of artificial intelligence in cardiovascular healthcare

‘Divided Tongues by Patrick Davidson Roberts’

A Review of ‘Divided Tongues' by Patrick Davidson Robert

Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets

Aims This study examines the ability of YOLO (You Only Look Once) 11x, a widely used and state of the art object detection model, trained on publicly available datasets, to identify and count neutrophils in tissue samples taken at prosthetic joint revision surgery, with the objective of automating a laborious but necessary part of the diagnostic workup for periprosthetic joint infection. Methods Three datasets containing blood film microscopic slides with neutrophils were downloaded, combined, and labelled. The resulting dataset of 3,923 images was augmented with ten additional histological slides from periprosthetic tissue, taken at the time of revision surgery (5 infected, 5 sterile), and split into training (70%), validation (20%), and test (10%) sets. The dataset was used to train YOLO 11x object detection model optimized for a mean average precision above 50%. The trained network was tested on a ground truth specimen and histological whole slide images from 19 additional cases, previously unseen by the model, for validation. The threshold for diagnosis of infection on histological sections was set at more than five neutrophils per 0.2 mm2 (equivalent to one high-powered microscope field). Results The model performed well as ground truth image returned precision at 82%, recall (sensitivity) 79%, and F1 harmonic mean 80%. When assessed against formal histopathological, microbiological, and multidisciplinary team (MDT) diagnosis, precision was 78%, 80%, and 90%; recall 78%, 89%, and 82%; and F1 score 78%, 84%, and 86%, respectively. Against the definitive MDT diagnosis, our model identified nine out of the ten infected cases and excluded seven out of nine cases that were not infected. Conclusion This study demonstrates ability of the trained model to identify neutrophils in tissue taken at revision surgery and could assist in diagnosis of periprosthetic infection. Further work is needed to improve confidence in the identifications and diagnostic accuracy of periprosthetic infection

Hidden Beats: Detection of Subclinical Atrial Fibrillation Using Questionnaires in Young Adults and Its Cardiovascular Risk Profile

Objectives The objectives were to identify subclinical AF in young individuals and to examine its association with cardiovascular risk profile and quality of life (QoL) in a young adult cohort. Design A cross-sectional survey was conducted with 403 adults aged 18-40 years in public and private hospitals. Methods The INTERHEART Modifiable Risk Score (IHMRS), Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire, and the Modified European Heart Rhythm Association (mEHRA) were used to collect data. The statistics were performed using descriptive statistics, Spearman's correlation, the Mann-Whitney U test, the Kruskal-Wallis test, and multiple linear regression in IBM SPSS 26. Results Of the 403 respondents, the majority were male (85%) and aged 36-40 (30%). A stronger negative correlation with QoL (0.42, p < 0.001) and a weaker positive correlation with cardiovascular risk (0.13, p = 0.009) were observed between higher EHRA symptom severity and cardiovascular risk. There was also a negative correlation between QoL and IHMRS (r = -0.29, p  = < 0.001). Women presented with greater symptoms and low QoL (p < 0.001). The regression analysis showed that predictors of better IHMRS were age, male gender, higher EHRA scores, physical inactivity, and a family history of CVD. In contrast, better QoL was a protective factor (p < 0.01). Conclusions Subclinical AF symptoms were highly correlated with increased cardiovascular risks and decreased QoL in young adults. Early detection of high-risk individuals, especially in settings with limited resources, could be achieved through simple questionnaire-based screening