Complex Questions Research Articles

Exploring GPT‐4.0's efficiency in handling paediatric appendicitis questions

AbstractObjectiveTo explore the potential and accuracy of the generative dialogue artificial intelligence tool GPT‐4.0 in answering questions related to paediatric emergency appendicitis.MethodsA cross‐sectional observational study design was used. We collected 134 appendicitis‐related questions from authoritative websites, such as Mayo Medical and APSA, covering all aspects of appendicitis, including simple and complex questions. These questions were answered by GPT‐4.0, and then evaluated by three paediatric surgical experts using a quality score ranging from 0 to 5. The answers were generated by GPT‐4.0 and then similarly evaluated by three experts for accuracy.ResultsWe found that GPT‐4.0 could achieve a high accuracy rate on simple questions with a quality score of 4.65 (standard deviation 0.51). For complex questions, the average score was 3.77 (standard deviation 0.68), and there was a significant difference between the two (P < .05). On clinical questions, the accuracy score of GPT‐4.0 was 4.00 (standard deviation 0.21). When answering actual questions from families of children with appendicitis, the accuracy score was 4.12 (standard deviation 0.59). Its accuracy lies between simple questions and complex questions, and it can basically meet the accuracy requirements of clinical questions. It's worth noting that GPT‐4.0 demonstrated empathy in answering some questions, which might further enhance patient satisfaction.ConclusionGPT‐4.0 showed its potential and accuracy in handling paediatric appendicitis questions, especially in simple and clinical questions. However, improvements are still needed in handling complex questions and updating information. Despite the limitations, this model is expected to improve the quality of medical services and enhance patient satisfaction.

Design-based methods for exploring ethical questions in the field of neurotechnologies

ABSTRACT Numerous complex and multi-faceted ethical questions arise from the innovation of neurotechnologies. Addressing these issues effectively requires the involvement of a diverse range of stakeholders, including patients, treatment providers, home users, scientists and engineers from different disciplines, and industry representatives. Different groups, however, possess varying levels of knowledge and experience regarding the ethical use and innovation of neurotechnologies. Therefore, customized methods are needed to identify their perspectives and ethical concerns. This article aims to introduce practical methods for eliciting ethical questions in the field of neurotechnology, including user journeys, persona approaches, material thinking, scenario building, fictional media contributions, and categorization.

Analisis Pengaruh Chatbot AI terhadap Pengalaman Mahasiswa Menggunakan E-commerce

This study analyzes the impact of Artificial Intelligence (AI) chatbot on college students' shopping experience in e-commerce. Results show that university students in Makassar gave positive responses to the chatbot service, praising its ability to handle complex questions, provide useful solutions, offer satisfactory assistance, solve problems efficiently, and understand user needs. The chatbot was considered highly responsive and easy to use. Positive impacts on customer satisfaction, including an improved shopping experience, improved customer service, and a desire to recommend and repurchase, were seen from the high average scores. These findings highlight the importance of AI chatbots in improving customer service and customer satisfaction in the competitive e-commerce market.

Establishing Legal Frameworks to Address Criminal Misuse of AI

Artificial Intelligence (AI) is transforming numerous sectors in India, including the legal field. As AI technology becomes more widespread, concerns arise regarding its impact on criminal liability. This abstract examines the legal challenges and implications of AI’s involvement in criminal activities within the Indian context. The convergence of AI and crime raises complex questions about assigning criminal responsibility. The autonomous functioning of AI algorithms complicates accountability, making it difficult to determine liability for AI-related offenses. It emphasizes the significance of data privacy and security in the context of AI-driven criminal acts. Criminals may misuse AI to exploit personal data, underscoring the need for stringent data protection laws to protect individual privacy rights. Furthermore, AI-generated false content, such as deepfakes, raises concerns about evidence authenticity and the potential for manipulation in legal processes. The abstract also considers ethical issues surrounding AI’s role in criminal acts, stressing the urgent need for India’s legal system to address these complex challenges. By establishing comprehensive legal guidelines, encouraging ethical AI development, and strengthening data privacy measures, India can address AI- related criminal risks and promote responsible, secure technological advancement within the framework of criminal liability. Key Words: AI Misuse, Cybercrime, Legal Frameworks, Indian Penal Code, Information Technology Act, AI regulation, Cyber Law, Machine Learning

Radiotherapy recommendation, initiation, and completion: Complex questions of access and equity

In their recent work, Hogan and colleagues shed light on predictors of radiation recommendation, initiation, and completion in the United States across 3 million patients. Further work should use qualitative and intersectional lenses to examine the root causes of poor access to radiation and identify ways to intervene. Steps forward should be multidisciplinary in scope, approaching radiation access through research, clinical interventions, and national policy.

Innovative clinical trials: Addressing the evolving needs of clinical research and participants with decentralised and complex trials

In general, innovation in clinical research often stems not only from aspiration – for example to increase accuracy and efficiency or to optimise costs – but also from shifts in social paradigms or from changes in circumstances that lead to changes in practices, as experienced during the COVID-19 pandemic. Indeed, the challenges clinical research faced during the pandemic led to changes in how clinical trials are conducted. Healthcare personnel had to come up with nontraditional ways to connect with their patients and patients’ families. Researchers had to develop new approaches since it was no longer possible to follow many everyday practices and because they had to answer even more complex questions – often with fewer resources. These challenges accelerated innovation in clinical research, particularly in the area of trial design. This article takes a deep dive into how design innovation has transformed clinical research by providing new operational approaches, such as those applied in decentralised clinical trials (PART 1), and new methodologies, such as those used in complex trials (PART 2).

RTA: A reinforcement learning-based temporal knowledge graph question answering model

Temporal Knowledge Graph Question Answering (TKGQA) is crucial research, focusing on finding an entity or a timestamp to answer temporal questions in the corresponding temporal knowledge graph. Currently, the main challenge in the temporal KGQA task is answering complex temporal questions, often necessitating complex multi-hop temporal reasoning in the TKG. In this paper, we propose a method for the TKGQA task called Reinforcement learning Temporal knowledge graph question Answering (RTA). First, in the question understanding stage, our model extracts context information to select topic entities of the given question, which can effectively deal with scenarios involving multiple entities in complex temporal questions. Furthermore, reasoning complexity escalates significantly with complex temporal questions, as varying timestamps alter the relations between entities. Therefore, we introduce reinforcement learning into the reasoning process. In the policy network, a dynamic path-matching module is specifically included to aggregate the features of relational paths to effectively capture the dynamic changes of the relations between entities on the reasoning paths. At the same time, the weights are calculated to obtain the degree of attention of each candidate action. Then the score of each candidate action is obtained through a weighted summation mechanism which helps the agent learn the optimal path reasoning policy for effective exploration. Finally, we evaluate our method on the CRONQUESTIONS dataset and validate its superiority over all baseline methods. Specifically, our approach proves effective in handling complex temporal questions.

A reflexion on the oxidative stress and animal welfare: a review.

A wide range of studies have documented the role of oxidative stress in the development of chronic pathological disorders and even in the aging itself. However, its significance to modern animal health and welfare remains neglected. Oxidative stress in biological systems refers to a disturbance in the balance between pro-oxidants and antioxidants in favour of the former, leading to potential damage of biomolecules. In farm animals, oxidative stress may be involved in several pathological conditions, including those that are relevant for animal production and the general welfare of the individuals, resulting in some cases in irreversible losses. The oxidative stress concept and how it may result in disease or be prevented are complex questions with no simple answers and therefore, call professionals for deep reflection, to maintain a high standard of animal welfare and production. The aim of this review was to gather relevant information on the characteristics of pro-oxidants and antioxidant as well as their significance in animal production systems.

Radiation Chemistry Reveals the Reaction Mechanisms Involved in the Reduction of Vinylene Carbonate in the Solid Electrolyte Interphase of Lithium-Ion Batteries.

A safe and efficient lithium-ion battery requires including an additive in the electrolyte. Among the additives used, vinylene carbonate (VC) is particularly interesting, because it leads to the formation of a stable and protective solid electrolyte interphase (SEI) on the negative electrode. However, the reduction behavior of VC, resulting in polymer formation, is complex, and many questions remain as to the corresponding reaction mechanisms. In particular, in conventional battery studies, it is not possible to observe the transient species formed during reduction. Using picosecond pulsed radiolysis coupled with theoretical chemistry calculations, we showed that, once formed, the anion radical VC·- can undergo ring opening in a few nanoseconds or form (VC)2·-.Within 100 ns, each of these anions then leads to the formation of (VC)(C3H2O3·-). This latter species starts oligomerizing. Eventually, a polymer is formed. Although it mainly consists of poly(VC) units, other chemical functions, such as alkyl groups, are also present, which highlights the role played by water, even in trace amounts. Lastly, we propose a scheme of the reaction mechanisms involved in VC reduction, leading to its polymerization. Clearly, the polymer formed from VC at the SEI of lithium-ion batteries has a complex structure.

A Comparative Analysis of Twelve Research Paradigms Across Six Knowledge Acquisitions and Twelve Sources of Knowledge: A Philosophical Discourse

This study investigates whether a single research paradigm can dominate knowledge acquisition across empirical and interpretive domains or if a multi-paradigm approach is essential for achieving a holistic understanding. Twelve paradigms, including Positivism, Pragmatism, Hermeneutics, and Feminism, were evaluated across six domains: Science, Philosophy, History, Art, Religion, and Culture. The methodology employed comparative analysis, factor analysis, sensitivity testing, and inter-rater reliability assessment to quantify paradigm performance. The findings confirm Positivism's strength in empirical fields like Science and Philosophy but reveal its limitations in addressing ethical, cultural, and interpretive dimensions, where paradigms like Hermeneutics and Feminism excel. Pragmatism and Critical Realism bridge empirical rigor with interpretive depth, offering integrative solutions for interdisciplinary challenges. By applying statistical tools within a philosophical inquiry, this research highlights the necessity of humility and open-mindedness in selecting paradigms to address complex questions. The study concludes that no single paradigm suffices across all domains, advocating for a multi-paradigm approach to foster collaboration, inclusivity, and a more comprehensive understanding of knowledge production. These insights have implications for interdisciplinary research, education, and policymaking, emphasizing the importance of integrating diverse epistemological perspectives.

Multifaceted Landscape ofOmics Data

Multifaceted Landscape ofOmics Data

Emotions and the Manifestation of Ancient Egyptian Royal Power: A Consideration of the Twin Stelae at Abu Simbel

Drawing on methods and theories from the history of emotions, this paper examines the Twin Stelae that flank the entrance into Ramses II’s Great Temple at Abu Simbel in order to investigate the feelings associated with ancient Egyptian kingship from an ideological perspective. As the ruler, what was the king himself supposed to feel, and what feelings was he meant to elicit in his subjects? How did the feelings of the king differ from those of his subjects, and how did all these feelings reinforce and reify the institution of kingship and royal power? In order to propose some answers to these complex questions, I offer a close reading of key words and passages on the Stelae, considering the choice of hieroglyphic signs that the artists used to write them, the ways in which the artists depicted these signs, and the context of the words and passages within the inscriptions. I then use the Stelae’s text to consider how ancient viewers were meant to see and experience the monumental façade of the Great Temple.

Functional Mitral Regurgitation Post-Isolated Aortic Valve Replacement.

Background: The management of mitral regurgitation during aortic valve replacement remains a complex question. Secondary mitral regurgitation often improves post-aortic valve replacement without mitral valve surgery, but residual mitral regurgitation can significantly affect long-term outcomes. This study investigates the natural history of mitral regurgitation following isolated aortic valve replacement and identifies prognostic factors for persistent mitral regurgitation. Methods: A retrospective study was conducted on 108 patients who underwent isolated aortic valve replacement. Patients were categorized based on mitral regurgitation improvement. Additionally, patients were divided into patient-prosthesis mismatch and non-patient-prosthesis mismatch groups based on the aortic prosthesis. Preoperative and postoperative echocardiographic data were analyzed. Results: In total, 63% of patients showed mitral regurgitation improvement. The improved functional MR group showed significant reductions in peak and mean transvalvular pressure gradients. In contrast, the patient-prosthesis mismatch group had persistent mitral regurgitation improvement in 59.2% of patients. The non-patient-prosthesis mismatch group exhibited significant structural improvements and a reduction in mitral regurgitation severity in 68.6% of patients. Conclusions: The study shows that aortic valve replacement could significantly improve MR when patient-prosthesis mismatch is avoided. This approach maximizes hemodynamic outcomes, mitigates the risk of residual or worsening mitral regurgitation, and potentially reduces the need for additional mitral valve interventions.

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We address the long-standing question of the computational complexity of determining homology groups of simplicial complexes, a fundamental task in computational topology, posed by Kaibel and Pfetsch over twenty years ago. We show that decision problem is QMA1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathsf{QMA}}}_{1}$$\\end{document}-hard and the exact counting version is #BQP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\#{\\mathsf{BQP}}$$\\end{document}-hard. In fact, we strengthen this by showing that the problems remains hard in the case of clique complexes, a family of simplicial complexes specified by a graph which is relevant to the problem of topological data analysis. The proof combines a number of techniques from Hamiltonian complexity and algebraic topology. As we discuss, a version of the problems satisfying a suitable promise and certain constraints is contained in QMA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathsf{QMA}}$$\\end{document} and #BQP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\#{\\mathsf{BQP}}$$\\end{document}, respectively. This suggests that the seemingly classical problem may in fact be quantum mechanical. We discuss potential implications for the problem of quantum advantage in topological data analysis.

Cities beyond proximity.

The concept of 'proximity-based cities' has gained attention as a new urban organizational model. Most prominently, the 15-minute city contends that cities can function more effectively, equitably and sustainably if essential, everyday services and key amenities are within a 15-minute walk or cycle. However, focusing solely on travel time risks overlooking disparities in service quality, as the proximity paradigm tends to emphasize the mere presence of an element in a location rather than bringing up more complex questions of identity, diversity, quality, value or relationships. Transitioning to value-based cities by considering more than just proximity can enhance local identity, resilience and urban democracy. Fostering bottom-up initiatives can create a culture of local care and value, while predominantly top-down governing strategies can lead to large inequalities. Balancing these approaches can maximize resilience, health and sustainability. This equilibrium has the potential to accompany sustainable growth, by encouraging the creation of innovative urban solutions and reducing inequalities.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Evaluation of ChatGPT as a Reliable Source of Medical Information on Prostate Cancer for Patients: Global Comparative Survey of Medical Oncologists and Urologists.

No consensus exists on performance standards for evaluation of generative artificial intelligence (AI) to generate medical responses. The purpose of this study was the assessment of Chat Generative Pre-training Transformer (ChatGPT) to address medical questions in prostate cancer. A global online survey was conducted April-June 2023 among >700 medical oncologists or urologists who treat patients with prostate cancer. Participants were unaware this was a survey evaluating AI. In component 1, responses to nine questions were written independently by medical writers (MW; from medical websites) and ChatGPT-4.0 (AI-generated from publicly available information). Respondents were randomly exposed and blinded to both AI-generated and MW-curated responses; evaluation criteria and overall preference were recorded. Exploratory component 2 evaluated AI-generated responses to five complex questions with nuanced answers in the medical literature. Responses were evaluated on a 5-point Likert scale. Statistical significance was denoted by P < .05. In component 1, respondents (N = 602) consistently preferred the clarity of AI-generated responses over MW-curated responses in 7/9 questions (P < .05). Despite favoring AI-generated responses when blinded to questions/answers, respondents considered medical websites a more credible source (52%-67%) than ChatGPT (14%). Respondents in component 2 (N = 98) also considered medical websites more credible than ChatGPT, but rated AI-generated responses highly for all evaluation criteria, despite nuanced answers in the medical literature. These findings provide insight into how clinicians rate AI-generated and MW-curated responses with evaluation criteria that can be used in future AI validation studies.

ChatGPT as a Source for Patient Information on Patellofemoral Surgery-A Comparative Study Amongst Laymen, Doctors, and Experts.

In November 2022, OpenAI launched ChatGPT for public use through a free online platform. ChatGPT is an artificial intelligence (AI) chatbot trained on a broad dataset encompassing a wide range of topics, including medical literature. The usability in the medical field and the quality of AI-generated responses are widely discussed and are the subject of current investigations. Patellofemoral pain is one of the most common conditions among young adults, often prompting patients to seek advice. This study examines the quality of ChatGPT as a source of information regarding patellofemoral conditions and surgery, hypothesizing that there will be differences in the evaluation of responses generated by ChatGPT between populations with different levels of expertise in patellofemoral disorders. A comparison was conducted between laymen, doctors (non-orthopedic), and experts in patellofemoral disorders based on a list of 12 questions. These questions were divided into descriptive and recommendatory categories, with each category further split into basic and advanced content. Questions were used to prompt ChatGPT in April 2024 using the ChatGPT 4.0 engine, and answers were evaluated using a custom tool inspired by the Ensuring Quality Information for Patients (EQIP) instrument. Evaluations were performed independently by laymen, non-orthopedic doctors, and experts, with the results statistically analyzed using a Mann-Whitney U Test. A p-value of less than 0.05 was considered statistically significant. The study included data from seventeen participants: four experts in patellofemoral disorders, seven non-orthopedic doctors, and six laymen. Experts rated the answers lower on average compared to non-experts. Significant differences were observed in the ratings of descriptive answers with increasing complexity. The average score for experts was 29.3 ± 5.8, whereas non-experts averaged 35.3 ± 5.7. For recommendatory answers, experts also gave lower ratings, particularly for more complex questions. ChatGPT provides good quality answers to questions concerning patellofemoral disorders, although questions with higher complexity were rated lower by patellofemoral experts compared to non-experts. This study emphasizes the potential of ChatGPT as a complementary tool for patient information on patellofemoral disorders, although the quality of the answers fluctuates with the complexity of the questions, which might not be recognized by non-experts. The lack of personalized recommendations and the problem of "AI hallucinations" remain a challenge. Human expertise and judgement, especially from trained healthcare experts, remain irreplaceable.

Efficacy of Complexity-Based Target Selection for Treating Morphosyntactic Deficits in Children With Developmental Language Disorder and Children With Down Syndrome: A Single-Case Experimental Design.

Selecting targets for morphosyntactic intervention is a critical component of treatment planning. The complexity approach suggests that, by treating a complex morphosyntactic target, improvements will occur for the treated structure and for related, simpler structures. This study evaluated the efficacy of the complexity approach for treating morphosyntactic deficits by targeting a complex BE verb question structure for children with developmental language disorder (DLD) and children with Down syndrome (DS) and observing its impact on treated and untreated BE verb structures. We also explored whether etiology impacted our participants' treatment responses. Three participants with DLD and three with DS received treatment for the BE verb question structure in the context of a single-case multiple-baseline design across participants. Accuracy of production for the treated structure and untreated BE verb structures was measured across baseline, treatment, and posttreatment phases. Treatment of the complex BE verb question structure resulted in change on the treated structure for three participants (i.e., two with DLD and one with DS). Generalization of treatment to untreated, related BE verb structures occurred for all six participants. Outcomes indicated participants from both etiologies benefited from treatment. This study provides evidence supporting the use of a complexity-based approach for selecting morphosyntactic treatment targets for children with DLD and children with DS. Additional research is needed to identify specific characteristics that may influence individual treatment responses. https://doi.org/10.23641/asha.27018124.

An evaluation of a visual interface for supporting query formulation in scholarly searching

Performing an effective literature review is a fundamental academic skill. Whether writing up a final project, preparing a research proposal, or simply finding answers to complex questions, students need to be able to systematically gather and analyse evidence from a variety of sources. However, many students have difficulty formulating effective search queries and understanding how to optimise their search strategy. This paper evaluates a novel approach to query formulation based on an alternative, visual interface. The approach is evaluated alongside a conventional, form-based interface in a comparative, controlled setting. The study finds that overall, participants considered the visual interface to be more complex and difficult to use. However, at the same time they used significantly more terms, facets and reformulations in the visual interface, and gave more attention to creating and refining more complex queries. In addition, the majority considered the visual interface to have greater transparency, with a clearer overview of the search tasks that encouraged them to invest greater effort in completing them successfully. The results demonstrate that visual approaches can offer tangible benefits in developing and improving students’ competence in exploratory search tasks.

Integrating real-world data and machine learning: A framework to assess covariate importance in real-world use of alternative intravenous dosing regimens for atezolizumab.

The increase in the availability of real-world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here we present a recently developed RWD/ML framework that utilizes ML algorithms to understand the influence and importance of various covariates on the use of a given dose and schedule for drugs that have multiple approved dosing regimens. To demonstrate the application of this framework, we present atezolizumab as a use case on account of its three approved alternative intravenous (IV) dosing regimens. As expected, the real-world use of atezolizumab has generally been increasing since 2016 for the 1200 mg every 3 weeks regimen and since 2019 for the 1680 mg every 4 weeks regimen. Out of the ML algorithms evaluated, XGBoost performed the best, as measured by the area under the precision-recall curve, with an emphasis on the under-sampled class given the imbalance in the data. The importance of features was measured by Shapley Additive exPlanations (SHAP) values and showed metastatic breast cancer and use of protein-bound paclitaxel as the most correlated with the use of 840 mg every 2 weeks. Although patient usage data for alternative IV dosing regimens are still maturing, these analyses provide initial insights on the use of atezolizumab and set up a framework for the re-analysis of atezolizumab (at a future data cut) as well as application to other molecules with approved alternative dosing regimens.