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Musculoskeletal surgeons use mixed reasoning rather than pure Bayesian strategies in clinical practice
Objectives To inform efforts to promote regular and normalized Bayesian reasoning, we studied factors associated with the degree to which surgeons use Bayesian reasoning to navigate uncertainty across different clinical scenarios. Methods Science of Variation Group members (153; 58% North America, 30% Europe, 69% over 15 years of experience) completed an online survey reading 8 scenarios of test and treatment decisions and chose one of 4 answer options with higher scores indicating more Bayesian reasoning. Internal consistency of the survey was assessed using Cronbach alpha. Results The average Bayesian reasoning score across all scenarios was 3.0 (IQR 2.7–3.2) on a 4-point scale, indicating a relative context-dependent variability. Completely non-Bayesian reasoning was selected least often (8.6%, 90 of 1,044) and fully Bayesian reasoning represented 29% (301 of 1,044) of responses. Most surgeons showed mixed patterns (defined as reasoning in which prior probability is acknowledged but underweighted, without explicit probabilistic updating): 85% (121 of 142) used fully Bayesian reasoning at least once (121 of 142) while 42% (60 of 142) used completely non-Bayesian reasoning at least once. The Cronbach alpha was 0.43 suggesting the scenarios measured different aspects of clinical reasoning rather a unified construct. Conclusions The finding that surgeons use relatively context-dependent reasoning suggests an opportunity for surgeons to develop and practice Bayesian reasoning strategies both in training programs and in practice.
Assessing spatio-temporal coupling between intangible cultural heritage and rural development in China (2013–2022) through coupling coordination analysis and random-forest prediction
Effect of spraying distance and arc current on the corrosion behavior of arc-sprayed Zn–15Al coatings for rolling stock components
IL-33 accelerates orthodontic tooth movement by promoting M1 differentiation of macrophages and osteoclast formation
Performance, safety, and limitations of multimodal large language models in wound image assessment
Microwave-induced modulation of intracellular distribution of peptides based on mitochondrial targeting sequences
Freedom of speech and civic participation predicts national commitments towards animal protection
A quantitative assessment of diagenetic controls in the lower Sarvak reservoir of an Iranian oil field, Zagros Basin
Bang! Exploding immune cells splatter potent toxins everywhere
Correction: The prevalence of mental health disorders and stress coping strategies among forced migrants from Ukraine and Russia
An intelligent drug supply chain management and recommendation framework using blockchain and TRPO-driven multi-agent learning
Carrier
Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation
Abstract Retinal lesion segmentation is one of the critical tasks to analyze retinal diseases. Many researchers have proposed deep-learning models to extract lesions from the retinal scans. However, these models often rely on image features that might not be clinically meaningful for identifying the retinal lesions. Additionally, these models need pixel-level ground truths, which are challenging to procure in the real world. To overcome these issues, we present a novel language-assisted multimodal convolutional transformer pipeline that aligns image features with the text features, where the text features are extracted from the prompts that contain clinically meaningful information about the retinal lesions, and the image features are generated from the retinal scans. This alignment between image and text features is established with one-time training using the proposed loss function. Afterward, the proposed network can robustly extract retinal lesions across different datasets at the inference stage. Moreover, since the proposed network infers learning from the text prompts, it does not require additional training rounds using pixel-level ground truth annotations to adapt to new datasets like the state-of-the-art methods. The proposed network is thoroughly tested on six public datasets, and it outperforms the state-of-the-art by achieving up to 7.77% improvements in terms of intersection-over-union.
Neovascularization of the Disk in Proliferative Diabetic Retinopathy
Design and optimization of a climate-resilient hybrid renewable microgrid for rural electrification in flood-affected regions
Abstract Seasonal flooding in the wetland regions of rural Bangladesh frequently disrupts national grid infrastructure, leaving hundreds of thousands of households without reliable electricity access for extended periods, a critical energy vulnerability that conventional grid extension strategies have consistently failed to resolve. To address this challenge, this study designs and optimizes a hybrid renewable energy microgrid combining solar photovoltaic (PV) panels, wind turbines (WT), and a battery energy storage system (BESS), connected to the existing grid via a power converter, to provide reliable and affordable electricity to 200 rural households in Mithamain Upazila, Kishoreganj District, Bangladesh. The system, modeled using HOMER Pro (version 3.14.2), achieves a competitive cost of energy of $0.02995/kWh, which is comparable to conventional rural electricity tariffs. The system’s net present cost is $107,712.60, with an initial investment of $48,107 and an annual operating cost of $895.93. Additionally, the system produces 73.9% of its energy from renewable sources, which reduces carbon dioxide emissions by 43,675 kg every year. Sensitivity analysis highlights the system’s strength in different weather and economic situations, confirming that hybrid microgrids are a feasible and resilient solution for energy access in rural areas that have been hit by floods. This research demonstrates that hybrid renewable energy microgrids are a long-term, low-cost solution for rural electrification in flood-prone areas.