Left versus right ventricular pacing during TAVR and balloon aortic valvuloplasty: A systematic review and meta-analysis

Introduction: While right ventricular pacing (RVP) is the conventional temporary pacing modality used for transcatheter aortic valve replacement (TAVR), this approach possesses inherent risks and procedural challenges. We aim to assess and compare the safety and efficacy of left ventricular pacing (LVP) and RVP during TAVR and balloon aortic valvuloplasty (BAV). Methods: Following PRISMA guidelines, a comprehensive literature search was conducted in four databases from inception to December 15th, 2023. We included observational studies and clinical trials comparing LVP with RVP during TAVR and BAV procedures. Primary outcomes included short-term mortality, mortality due to cardiac tamponade, and procedural complications including bleeding, vascular complications, and cardiac tamponade. Secondary outcomes comprised procedure duration and length of hospital stay. Results: Five studies involving 830 patients with RVP and 1577 with LVP were included. Short-term mortality was significantly higher in the RVP group (RR 2.32, 95% CI: [1.37–3.93], P = .002), as was the incidence of cardiac tamponade (RR 2.19, 95% CI: [1.11–4.32], P = .02). LVP demonstrated shorter hospital stays (MD = 1.34 d, 95% CI: [0.90, 1.78], P < .001) and reduced procedure duration (MD = 7.75 min, 95% CI: [5.08, 10.41], P < .00001) compared to RVP. New pacemaker implantation was higher in the RVP group (RR 2.23, 95% CI: [1.14, 4.39], P = .02). Conclusion: LVP during TAVR and BAV emerges a safer alternativ

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Minimum 10-Year Outcomes of Adolescents Undergoing Contemporary Hip Arthroscopic Surgery for Femoroacetabular Impingement Syndrome

(Abstract not found)

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Hybrid cognitive authority and algorithmic subjectivity: rethinking knowledge management in AI-driven communication

Purpose – This paper aims to explore the transformative impact of generative artificial intelligence (AI) and human-computer interaction (HCI) on knowledge management in business, focusing on how AIdriven communication reshapes organizational practices. It examines the role of HCI in designing usercentric AI tools and introduces Hybrid Cognitive Authority (the co-construction of knowledge between human and AI agents) and Algorithmic Subjectivity (AI-generated communication simulating intent without cognition) to evaluate their effects on decision-making, knowledge flows and ethical governance in commercial settings. Design/methodology/approach – A mixed-methods approach integrates interdisciplinary genealogical analysis, qualitative case studies and critical discourse analysis. The study traces the evolution of commercial communication from mid-20th-century pragmatics to AI-mediated paradigms, synthesizing insights from cognitive science, information systems and digital epistemology. Five case studies of AI and HCI applications in business (e.g. customer service, recruitment) and discourse analysis of AI-generated artifacts provide empirical evidence for assessing knowledge management outcomes. Findings – Generative AI, supported by HCI, enhances knowledge management by improving efficiency and scalability, but raises challenges related to transparency, algorithmic bias and accountability. Empirical cases demonstrate how hybrid human-AI systems optimize knowledge processes while highlighting ethical risks, such as biased outputs. The proposed framework of Hybrid Cognitive Authority and Algorithmic Subjectivity necessitates governance models that balance AI automation with human oversight to maintain trust and interpretive agency. Orig inality/value – Unlike prior studies that view AI as a passive tool, this research positions AI as an active knowledge co-constructor, advancing knowledge management scholarship through the novel concepts of Hybrid Cognitive Authority and Algorithmic Subjectivity. It bridges theory and practice by offering actionable strategies for businesses to leverage AI and HCI responsibly, contributing to economic efficiency, ethical governance and societal trust in AI-driven communication.

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Humble leadership in development volunteer management in China: its

Purpose – This study aims to explore the impact of humble leadership on job crafting and job satisfaction among development volunteers, who contribute time and skills to support disadvantaged communities. Specifically, it examines how humble leaders motivate volunteers to actively adjust their work, leading to increased job satisfaction. This research fills a gap in understanding the role of humble leadership in development volunteer management. Design/methodology/approach – This study utilized a quantitative research methodology with snowball sampling to collect data from 334 volunteers who participated in a development volunteering program in rural China. Four variables – humble leadership, job crafting, job satisfaction and volunteer role identity – were measured using translated scales . The data were analyzed using descriptive statistics, confirmatory factor analysis, correlation analysis, structural equation modeling and bootstrapping. Findings – Humble leaders encouraged volunteers to shape their work, enhancing engagement and satisfaction. A stronger role identity strengthened the influence of humble leaders on job crafting, further increasing satisfaction. The findings suggest that humble leaders promote volunteers’ long-term efficacy by recognizing their individual contributions and creating an open and supportive work environment. Research limitations/implications – The study provides practical implications for volunteer management. Organizations may benefit from encouraging humble leadership and enhancing volunteers’ role identity through training and selection. However, focusing on a single volunteering program in China may limit the generalizability of the findings. Future studies should explore contextual differences across regions. Originality/ value – This study highlights the importance of humble leadership in volunteer management, particularly in enhancing leadership’s role in long-term mission-driven projects.

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixedlanguage content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks.

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