Search Results (50)

Large language models (LLMs) rapidly transform healthcare by automating tasks, streamlining administration, and enhancing clinical decision support. This rapid review assesses current and emerging applications of LLMs in diagnostic-related group (DRG) assignment and clinical decision support systems (CDSS), with emphasis on radiology and nuclear medicine. Evidence shows that LLMs, particularly those tailored for medical domains, improve efficiency and accuracy in DRG coding and radiology report generation, providing clinicians with actionable, context-sensitive insights by integrating diverse data sources. Advances like retrieval-augmented generation and multimodal architecture further increase reliability and minimize incorrect or misleading results that AI models generate, a term that is known as hallucination. Despite these benefits, challenges remain regarding safety, explainability, bias, and regulatory compliance, necessitating ongoing validation and oversight. The review prioritizes recent, peer-reviewed literature on radiology and nuclear medicine to provide a practical synthesis for clinicians, administrators, and researchers. While LLMs show strong promise for enhancing DRG assignment and radiological decision-making, their integration into clinical workflows requires careful management. Ongoing technological advances and emerging evidence may quickly change the landscape, so findings should be interpreted in context. This review offers a timely overview of the evolving role of LLMs while recognizing the need for continuous re-evaluation. Full article

Neuroendocrine neoplasms (NENs) of the lung are a biologically and clinically diverse group of tumors that includes well-differentiated typical and atypical carcinoids (LNETs), as well as poorly differentiated large-cell neuroendocrine carcinoma and small-cell lung cancer. Despite their relative rarity, the incidence of LNETs is increasing, primarily due to advancements in diagnostic techniques and heightened clinical awareness. While the current World Health Organization (WHO) classification offers a morphological basis for diagnosis and prognosis, particularly for extrapulmonary neuroendocrine neoplasms (ep-NENs), it has limitations in predicting the clinical behavior of pulmonary carcinoids. Recent evidence highlights the inadequacy of traditional criteria in fully capturing the biological complexity and clinical heterogeneity of these tumors. This review explores the evolving landscape of LNETs, focusing on well-differentiated forms and analyzing current classification systems, clinicopathological features, and the emerging role of novel prognostic and predictive biomarkers. Advances in histopathology and molecular profiling have begun to elucidate distinct molecular subsets within carcinoids, offering potential avenues for improved risk stratification and therapeutic decision-making. Although there are limited treatment options for advanced disease, new insights into tumor biology could facilitate the development of personalized therapeutic strategies and pave the way for future innovations in LNET management. Full article

Background/Objective: The COVID-19 pandemic profoundly transformed social life worldwide, indiscriminately affecting individuals across all age groups. Children have not been exempted from the risk of severe illness and death caused by COVID-19. Objective: This paper sought to describe the clinical findings, laboratory and imaging results, and hospital care provided for severe and critical cases of COVID-19 in unvaccinated children, with or without severe asthma, hospitalized in a public referral service for COVID-19 treatment in the Brazilian state of Pernambuco. Methods: This was a case series study of severe and critical COVID-19 in hospitalized, unvaccinated children, with or without severe asthma, conducted in a public referral hospital between March 2020 and June 2021. Results: The case series included 80 children, aged from 1 month to 11 years, with the highest frequency among those under 2 years old (58.8%) and a predominance of males (65%). Respiratory diseases, including severe asthma, were present in 73.8% of the cases. Pediatric multisystem inflammatory syndrome occurred in 15% of the children, some of whom presented with cardiac involvement. Oxygen therapy was required in 65% of the cases, mechanical ventilation in 15%, and 33.7% of the children required intensive care in a pediatric intensive care unit. Pulmonary infiltrates and ground-glass opacities were common findings on chest X-rays and CT scans; inflammatory markers were elevated, and the most commonly used medications were antibiotics, bronchodilators, and corticosteroids. Conclusions: This case series has identified key characteristics of children with severe and critical COVID-19 during a period when vaccines were not yet available in Brazil for the study age group. However, the persistence of low vaccination coverage, largely due to parental vaccine hesitancy, continues to leave children vulnerable to potentially severe illness from COVID-19. These findings may inform the development of public health emergency contingency plans, as well as clinical protocols and care pathways, which can guide decision-making in pediatric care and ensure appropriate clinical management, ultimately improving the quality of care provided. Full article

Cardiogenic shock (CS) in the setting of severe aortic stenosis (AS) presents a critical and high-risk scenario with limited therapeutic options and poor prognosis. Transcatheter aortic valve implantation (TAVI), initially reserved for inoperable or high-risk surgical candidates, is increasingly being considered in patients with CS due to improvements in device technology, operator experience, and supportive care. This review synthesizes current evidence from large registries, observational studies, and meta-analyses that support the feasibility, safety, and potential survival benefit of urgent or emergent TAVI in selected CS patients. Procedural success is high, and early intervention appears to confer improved short-term and mid-term outcomes compared to balloon aortic valvuloplasty or medical therapy alone. Critical factors influencing prognosis include lactate levels, left ventricular ejection fraction, renal function, and timing of intervention. The absence of formal guidelines, logistical constraints, and ethical concerns complicate decision-making in this unstable population. A multidisciplinary Heart Team/Shock Team approach is essential to identify appropriate candidates, manage procedural risk, and guide post-intervention care. Further studies and the development of TAVI-specific risk models in CS are anticipated to refine patient selection and therapeutic strategies. TAVI may represent a transformative option for stabilizing hemodynamics and improving outcomes in this otherwise high-mortality group. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

The emergence and popularity of social media have made large-scale group decision-making (LSGDM) problems increasingly common, resulting in significant research interest in this field. LSGDM involves numerous evaluators, which can lead to disagreements and hesitancy among them. Hesitant fuzzy sets (HFSs) become crucial in this context as they capture the uncertainty and hesitancy among evaluators. On the other hand, research on the Consensus Reaching Process (CRP) becomes particularly important in dealing with the inevitable differences among the great number of evaluators. Ways to mitigate these differences to reach an agreement are a crucial area of study. For this reason, this paper presents a new CRP model to deal with LSGDM problems in hesitant fuzzy environments. First, HFSs and Normal-type Hesitant Fuzzy Sets (N-HFSs) are introduced to integrate evaluators’ subgroup and collective opinions, aiming to preserve as much decision information as possible while reducing computational complexity. Subsequently, a CRP with a detailed feedback suggestion generation mechanism is developed, which considers the willingness of evaluators to modify their opinions, thereby improving the effectiveness of reaching an agreement. Finally, a LSGDM framework that does not require any normalization process is proposed, and its feasibility and robustness are demonstrated through a numerical example. Full article

Background: Cochlear implants (CIs) have become a widely used intervention for Deaf and hard-of-hearing (DHH) children, particularly for developing spoken language. However, parental decision-making regarding CIs is influenced by a range of factors, including socio-economic status, healthcare accessibility, cultural beliefs, and societal attitudes. While extensive research on parental perceptions of CIs exists in high-income countries (HICs), there is limited research on these perspectives in low- and middle-income countries (LMICs), like South Africa, where disparities in healthcare access significantly impact CI uptake. Objectives: This study aimed to explore the views and perceptions of South African parents regarding CIs for their DHH children, with a specific focus on how financial, cultural, and informational barriers influence decision-making. Methods: A mixed-methods approach was used, combining Q-methodology for quantitative data and thematic analysis for qualitative insights. Nine parents of DHH children participated. The Q-set survey ranked parental attitudes toward CI risks, benefits, and accessibility, while semi-structured interviews provided deeper insights into decision-making processes. Factor analysis grouped participants into clusters based on their perceptions, and qualitative data were analysed using a thematic framework approach. Results: Findings revealed two distinct parental clusters: (a) Cluster 1 parents viewed CIs as essential for speech development and strongly supported implantation, and (b) Cluster 2 parents recognized CI benefits but emphasized that outcomes vary based on individual circumstances. Three overarching themes emerged from thematic analysis: (1) financial barriers restricting CI access, (2) parental reliance on medical professionals for decision-making, and (3) persistent stigma and cultural beliefs influencing CI perceptions. Conclusions: This study highlights critical barriers to CI access in South Africa, including socio-economic inequities, limited healthcare infrastructure, and persistent stigma. While parents largely recognized the benefits of CIs, their decisions were shaped by financial constraints and concerns about Deaf identity and societal acceptance. This study calls for the expansion of publicly funded CI programmes, the development of culturally tailored parental counselling protocols, and targeted public awareness campaigns to reduce stigma surrounding hearing restoration devices. These interventions can help mitigate financial and cultural barriers to CI adoption in South Africa. Full article

In recent years, the frequent occurrence of public emergencies has often triggered the rapid spread and amplification of social emotions. The accumulation and intensification of negative emotions can lead to collective behaviors and even pose a threat to social stability. To better understand the formation and evolution of social emotions in such contexts, this study constructs a theoretical framework and simulation approach that combines opinion dynamics with emotional and trust interactions. First, we propose a clustering method that incorporates emotional similarity and trust relationships among users to delineate group structures involved in social emotion formation. Second, a dynamic trust adjustment mechanism is also proposed to capture how trust evolves as individuals interact emotionally. Third, a large-scale group emotional consensus decision-making approach, based on the DeGroot model, is developed to simulate how emotional exchanges and resonance drive groups toward consensus in public emergencies. Additionally, we present a strategy for guiding emotional interactions to reach a desired consensus that ensures minimal modifications to collective preference values while achieving an acceptable consensus level, helping to manage emotional escalation. To validate the proposed model, we conduct simulations using the “Fat Cat” incident as a case study. The results reveal key mechanisms underlying social emotion formation during public emergencies and highlight critical influencing factors, including user participation, opinion leader influence, and trust relationships. This study provides a clear understanding of how social emotions are generated and offers practical insights for managing emotional dynamics and improving group decision-making during crises. Full article

To tackle preference conflicts and uncertainty in large-group emergency decision-making (LGEDM), this study proposes a probabilistic linguistic LGEDM method integrating the Louvain algorithm and group pressure model. First, expert weights are determined based on a social trust network, and the Louvain algorithm is employed for expert clustering, reducing the complexity of large-scale decision information. Second, a group pressure model is introduced to dynamically adjust expert preferences, enhancing consensus and decision consistency. Third, probabilistic linguistic term sets (PLTSs) are utilized to represent fuzzy and uncertain information, while attribute weights are determined by incorporating both subjective and objective factors, ensuring scientific rigor in decision-making. Finally, an improved TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision-Making) method is adopted to account for the loss aversion behavior of decision-makers (DMs), enabling a more accurate characterization of psychological decision-making traits. The experimental results demonstrate that the proposed method outperforms existing approaches in terms of decision efficiency, group consensus, and result robustness, offering effective support for emergency decision-making in crisis situations. Full article

Background/Objectives: The prognostic value of baseline clinical parameters in predicting the survival prolonging effect of Radium-223-dichloride (²²³RaCl₂) for metastatic castration resistant prostate cancer (mCRPC) patients has been the object of intensive research and remains an open issue. This national multicenter study aimed to corroborate the evidence of ten years of clinical experience with ²²³RaCl₂ by collecting data from eight Italian Nuclear Medicine Units. Methods: Data from 581 consecutive mCRPC patients treated with ²²³RaCl₂ were retrospectively analyzed. Several baseline variables relevant to the overall survival (OS) analysis were considered, including age, previous radical prostatectomy/radiotherapy, number of previous treatment lines, prior chemotherapy, Gleason score, presence of lymphoadenopaties, number of bone metastases, concomitant use of bisphosphonates/Denosumab, Eastern Cooperative Oncology Group Performance Status (ECOG-PS), as well as baseline values of hemoglobin (Hb), platelets, Total Alkaline Phosphatase (tALP), Lactate Dehydrogenase (LDH), and Prostate-Specific Antigen (PSA). Data were summarized using descriptive statistics, univariate analysis and multivariate analysis with the Cox model. Results: The median OS time was 14 months (95%CI 12–17 months). At univariate analysis age, the number of previous treatment lines, number of bone metastases, ECOG-PS, presence of lymphadenopathies at the time of enrollment, as well as baseline tALP, PSA, and Hb, were independently associated with OS. After multivariate analysis, the number of previous treatment lines (HR = 1.1670, CI = 1.0095–1.3491, p = 0.0368), the prior chemotherapy (HR = 0.6461, CI = 0.4372–0.9549, p = 0.0284), the presence of lymphadenopathies (HR = 1.5083, CI = 1.1210–2.0296, p = 0.0066), the number of bone metastases (HR = 0.6990, CI = 0.5416–0.9020, p = 0.0059), ECOG-PS (HR = 1.3551, CI = 1.1238–1.6339, p = 0.0015), and baseline values of tALP (HR = 1.0008, CI = 1.0003–1.0013, p = 0.0016) and PSA (HR = 1.0004, CI = 1.0002–1.0006, p = 0.0005) remained statistically significant. Conclusions: In the era of precision medicine and in the landscape of novel therapies for mCRPC, the prognostic stratification of patients undergoing ²²³RaCl₂ has a fundamental role for clinical decision-making, ranging from treatment choice to optimal sequencing and potential associations. This large Italian multicenter study corroborated the prognostic value of several variables, emerging from ten years of clinical experience with ²²³RaCl₂. Full article

Background and Objectives: Despite advances in surgery and perioperative chemotherapy, locally advanced gastric cancer continues to pose significant challenges, creating a pressing need for biomarkers capable of predicting therapeutic efficacy and survival outcomes. This study evaluates the prognostic and predictive significance of large unstained cells (LUCs), a morphologically distinct subset of white blood cells identified in peripheral blood that remain unstained by standard hematological dyes, as potential indicators of immune competence and treatment response. Materials and Methods: This retrospective analysis included patients diagnosed with locally advanced gastric cancer (cT2-4, N0-3) at Ankara Bilkent City Hospital between January 2018 and November 2024. Primary endpoints were overall survival (OS) and disease-free survival (DFS), stratified by LUC levels. The secondary endpoint was the association between LUC levels and pathological tumor response. Results: A total of 180 patients were analyzed, with a median age of 59 years; a total of 76% were male. The median follow-up period was 16.5 months, during which OS and DFS rates were 82% and 66%, respectively. Most patients were presented with advanced-stage disease, including T3–T4 tumors (91%) and nodal positivity (81%). Stratification by LUC levels revealed significantly shorter DFS (HR: 2.12; 95% CI: 1.12–4.01; p = 0.020) and OS (HR: 3.37; 95% CI: 1.26–9.03; p = 0.015) in the low-LUC group compared to the high-LUC group. Furthermore, the high-LUC group exhibited a significantly higher tumor shrinkage rate (ypN0: 60% vs. 44%; p = 0.020), although tumor regression scores were similar across groups. Advanced tumor stage and lack of pathological response were strongly associated with reduced DFS and OS, while poorly cohesive carcinoma histology emerged as a predictor of inferior OS. Conclusions: This study demonstrates that elevated LUC levels are significantly associated with improved DFS and OS, as well as enhanced tumor shrinkage, in patients with locally advanced gastric cancer. These findings show the potential of LUCs as a promising biomarker for prognostication and therapeutic stratification in this population, offering a novel avenue for refining clinical decision-making. Further validation through prospective investigations is warranted. Full article

Fuzzy semantics have a wide range of applications in life, and especially when expressing people’s evaluation information, it is more specific. As people increasingly prefer to express their personal opinions through media platforms, the opinions of the general public have become an indispensable reference. However, information asymmetry can have a significant impact on the rationality of decision-making. Based on the above considerations, this paper extends bidirectional projection to probabilistic linguistic term sets to preserve the completeness of information as much as possible. The large-scale group decision-making problem under the probabilistic linguistic environment is extended to limited interval values, and a new group decision-making method named IVBPRT-ELECTRE III algorithm (ELECTRE III based on bidirectional projection and regret theory under limited interval-valued probabilistic linguistic term set) is proposed. The method is an extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method based on limited interval-valued probabilistic linguistic term set (l-IVPLTS) bidirectional projection by regret theory approach. Firstly, this involves mining the online text comment information on social media about an emergency and considering the effect of the number of fans, determining the attributes and their initial weights for judging the strengths and weaknesses of the emergency management alternative using the TF-IDF and the $\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{d}2\mathrm{v}\mathrm{e}\mathrm{c}$ technology, and using the entropy value to adjust the initial weight of attributes, not only considering the real opinions of the public, but also combining with the views of experts, making the decision-making alternative selection more scientific and reasonable. Secondly, this paper fills the gap of bidirectional projection under l-IVPLTS environment; then, combining l-IVPLTS bidirectional projection and regret theory to determine the objective weights of experts, combines the differences in individual expertise of experts to obtain the comprehensive weights of experts, and uses the extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method to rank the alternatives. Finally, the feasibility and validity of the provided method is verified through the Yanjiao explosion incident as a case. Full article

Huddling behaviour is observed across various mammalian and avian species. Huddling, a behaviour wherein animals maintain close physical contact with conspecifics for warmth and social bonding, is widely documented among species in cold environments as a crucial thermoregulatory mechanism. Interestingly, on Shodoshima, Japanese macaques form exceptionally large huddling clusters, often exceeding 50 individuals, a significant deviation from the smaller groups observed in other populations (Arashyama, Katsuyama, and Taksakiyama) and climates. This study aims to uncover the mechanisms behind the formation and size of these huddling clusters, proposing that such behaviours can be explained by simple probabilistic rules influenced by environmental conditions, the current cluster size, and individual decisions. Employing a computational model developed in Netlogo, we seek to demonstrate how emergent properties like the formation and dissolution of clusters arise from collective individual actions. We investigate whether the observed differences in huddling behaviour, particularly the larger cluster sizes on Shodoshima compared to those in colder habitats, reflect variations in social tolerance and cohesion. The model incorporates factors such as environmental temperature, cluster size, and individual decision-making, offering insights into the adaptability of social behaviours under environmental pressures. The findings suggest that temperature plays a crucial role in influencing huddling behaviour, with larger clusters forming in colder climates as individuals seek warmth. However, the study also highlights the importance of joining and leaving a cluster in terms of probability in the dynamics of huddling behaviour. We discussed the large clusters on Shodoshima as a result of a combination of environmental factors and a unique social tolerance and cohesion among the macaques. This study contributes to our understanding of complex social phenomena through the lens of self-organisation, illustrating how simple local interactions can give rise to intricate social structures and behaviours. Full article

With the increasing share of renewable energy in the grid and the enhanced flexibility of the future power system, it is imperative for thermal power companies to explore alternative strategies. The flexible transformation of thermal power units is an effective strategy to address the previously mentioned challenges; however, the factors influencing the diffusion of this technology merit further investigation, yet they have been seldom examined by scholars. To address this gap, this issue is examined using an evolutionary game model of multi-agent complex networks, and a more realistic group structure is established through heterogeneous group differentiation. With factors such as group relationships, diffusion paths, compensation electricity prices, and subsidy intensities as variables, several diffusion scenarios are developed for research purposes. The results indicate that when upper-level enterprises influence the decision-making of lower-level enterprises, technology diffusion is significantly accelerated, and enhanced communication among thermal power enterprises further promotes diffusion. Among thermal power enterprises, leveraging large and medium-sized enterprises to promote the flexibility transformation of units proves to be an effective strategy. With regard to factors like the compensation price for depth peak shaving, the initial application ratio of groups, and the intensity of government subsidies, the compensation price emerges as the key factor. Only with a high compensation price can the other two factors effectively contribute to promoting technology diffusion. Full article

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers’ willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations. Full article