The Eye-Opening Truth About Private Surgical Facilities in Canada.

This paper examines the contentious issue of using contracted surgical facilities (CSFs) for scheduled eye surgeries within Canada's publicly funded healthcare system. Despite the debate over the use of CSFs, there is a stark lack of Canadian-focused empirical evidence to guide policy decisions. This paper uses the Organisation for Economic Co-operation and Development's healthcare system performance conceptual model - access, quality and cost/expenditures - as a framework to explore the debates surrounding CSFs. It highlights the mixed evidence from international studies and proposes recommendations for policy makers to ensure equitable access, maintain high-quality care and achieve cost-effectiveness. The paper underscores the necessity for informed policy making supported by robust empirical research, stakeholder engagement and continuous policy evaluation to address the challenges posed by the integration of CSFs into Canada's healthcare landscape.

Artificial intelligence in the classification and segmentation of fundus images with choroidal nevi.

OBJECTIVE: The purpose of this study is to summarize the results from 3 experimental studies into the use of artificial intelligence to classify and segment colour fundus images with choroidal nevi. STUDY DESIGN: This study is based on a secondary analysis of colour fundus images taken of patients receiving usual clinical care from the Alberta Ocular Brachytherapy Program. METHODS: High-resolution colour fundus images were labeled by experienced ocular oncologists. In experimental study 1, four pre-trained models (ResNet 50, VGG-19, VGG-16, and AlexNet) were evaluated for their ability to classify images based on the presence of choroidal nevi. In experimental study 2, the performance of 3 patch-based models to classify images based on the presence of choroidal nevi were compared. In experimental study 3, four convolutional neural network models were developed to segment the images. In experimental studies 1 and 2, performance was measured using accuracy, precision, recall, F1 score, and AUC. In experimental study 3, IoU and Dice measures were used to evaluate performance. RESULTS: A total of 591 labelled colour fundus images were used for analysis. In experimental study 1, VGG-16 showed the best accuracy, AUC, and recall, but lower precision in classifying images. In experimental study 2, the patched approached enhanced with artifact and contrast outperformed the others in classifying images. In experimental study 3, a voting-based Ensemble model excelled in segmenting the part of images with nevi. CONCLUSIONS: It is feasible to train AI models to identify choroidal nevi in colour fundus images.

Earliest Olduvai hominins exploited unstable environments ~ 2 million years ago.

Rapid environmental change is a catalyst for human evolution, driving dietary innovations, habitat diversification, and dispersal. However, there is a dearth of information to assess hominin adaptions to changing physiography during key evolutionary stages such as the early Pleistocene. Here we report a multiproxy dataset from Ewass Oldupa, in the Western Plio-Pleistocene rift basin of Olduvai Gorge (now Oldupai), Tanzania, to address this lacuna and offer an ecological perspective on human adaptability two million years ago. Oldupai's earliest hominins sequentially inhabited the floodplains of sinuous channels, then river-influenced contexts, which now comprises the oldest palaeolake setting documented regionally. Early Oldowan tools reveal a homogenous technology to utilise diverse, rapidly changing environments that ranged from fern meadows to woodland mosaics, naturally burned landscapes, to lakeside woodland/palm groves as well as hyper-xeric steppes. Hominins periodically used emerging landscapes and disturbance biomes multiple times over 235,000 years, thus predating by more than 180,000 years the earliest known hominins and Oldowan industries from the Eastern side of the basin.