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Beyond the splint: efficacy and safety of kinesio taping for non-operative management of finger injuries
BACKGROUND: This prospective comparative study aimed to evaluate the efficacy of kinesio-taping (KT), buddy taping (BT), and conventional splinting in managing finger joint sprains and fractures across pediatric and adult populations, with a focus on functional recovery and patient-centered outcomes. →
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The efficacy and safety of hydroxychloroquine in patients with chronic inflammatory cardiomyopathy: a multicenter randomized study (HYPIC trial)
CONCLUSIONS: Twelve months of HCQ combined with PDN therapy significantly improved the prognosis and heart function, inhibited inflammation, and had acceptable safety in patients with infl-CMP after FM. →
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Callback time preference for prescreening visits among Black residents in the Boston area: findings from two randomized controlled trials
CONCLUSION: Contacting participants during their preferred callback time was associated with reaching them and completing a prescreening interview. These data highlight the importance of study teams’ flexibility in outreach time to enhance the recruitment of Black adults into cardiovascular clinical trials. →
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Cost-effectiveness of rapid, ICU-based, syndromic PCR in hospital-acquired pneumonia: analysis of the INHALE WP3 multi-centre RCT
CONCLUSIONS: We found lower average ICU costs with the Pneumonia Panel. The pneumonia panel was cost-effective in terms of antibiotic stewardship, but not clinical cure. →
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Sleep quality patterns in patients with heart failure: a person-centred latent class analysis from a secondary analysis of the MOTIVATE-HF trial
CONCLUSIONS: Patients with HF exhibit heterogeneous sleep quality patterns with specific clinical and psychological profiles. These findings highlight the need for personalised interventions, systematic sleep assessments and the integration of cardiac rehabilitation strategies into standard HF care. →
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Mixture-of-Agents (MoA): A Breakthrough in LLM Performance
Mixture-of-Agents (MoA): A Breakthrough in LLM Performance The Mixture-of-Agents (MoA) architecture enhances large language model (LLM) performance, particularly on complex, open-ended tasks where a single model may struggle with accuracy, reasoning, or domain specificity. Understanding the Target Audience The target audience for MoA includes: AI Researchers: Seeking advanced methodologies to improve LLM capabilities. Business Leaders:… →
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FAQs: Everything You Need to Know About AI Agents in 2025
FAQs: Everything You Need to Know About AI Agents in 2025 FAQs: Everything You Need to Know About AI Agents in 2025 TL;DR Definition: An AI agent is an LLM-driven system that perceives, plans, uses tools, acts inside software environments, and maintains state to reach goals with minimal supervision. Maturity in 2025: Reliable on narrow,… →
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Technical Deep Dive: Automating LLM Agent Mastery for Any MCP Server with MCP- RL and ART
«`html Technical Deep Dive: Automating LLM Agent Mastery for Any MCP Server with MCP- RL and ART Introduction Empowering large language models (LLMs) to fluidly interact with dynamic, real-world environments is a new frontier for AI engineering. The Model Context Protocol (MCP) specification offers a standardized gateway through which LLMs can interface with arbitrary external… →
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Alibaba Qwen Unveils Qwen3-4B-Instruct-2507 and Qwen3-4B-Thinking-2507: Refreshing the Importance of Small Language Models
Alibaba Qwen Unveils Qwen3-4B-Instruct-2507 and Qwen3-4B-Thinking-2507: Refreshing the Importance of Small Language Models Alibaba’s Qwen team has introduced two new small language models: Qwen3-4B-Instruct-2507 and Qwen3-4B-Thinking-2507. Despite their 4 billion parameter count, these models demonstrate impressive capabilities across various tasks while maintaining efficiency on consumer-grade hardware. Both models come with native 256K token context windows,… →
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VL-Cogito: Advancing Multimodal Reasoning with Progressive Curriculum Reinforcement Learning
VL-Cogito: Advancing Multimodal Reasoning with Progressive Curriculum Reinforcement Learning Understanding the Target Audience The primary audience for VL-Cogito includes AI researchers, business leaders in technology, and educators interested in the advancements of multimodal reasoning and reinforcement learning. Their pain points revolve around the challenges of integrating diverse data sources, improving model accuracy, and addressing the… →