Support Tool Research Articles

Establishing methodological standards for the development of artificial intelligence-based Clinical Decision Support in emergency medicine.

Artificial intelligence (AI) offers opportunities for managing the complexities of clinical care in the emergency department (ED), and Clinical Decision Support has been identified as a priority application. However, there is a lack of published guidance on how to rigorously develop and evaluate these tools. We sought to answer the question, "What methodological standards should be applied to the development of AI-based Clinical Decision Support tools in the ED?". We conducted an iterative consensus-establishing activity involving a subcommittee with AI expertise followed by surveys and a live facilitated discussion with participants of the 2024 Canadian Association of Emergency Physicians Research Symposium in Saskatoon. We augmented analysis of participant feedback with large language models. We established 11 recommendations AI-based Clinical Decision Support development including the selection of a relevant problem and team of experts, standards of data quality and quantity, novel AI-specific reporting guidelines, and adherence to principles of ethics and privacy. We removed the recommendation regarding model interpretability from the final list due to a lack of consensus. These 11 recommendations provide guiding principles and methodological standards for emergency medicine researchers to rigorously develop AI-based Clinical Decision Support tools and for clinicians to gain knowledge and trust in using them.

The mechanical test as a supplemental decision support tool for the safe removal of an Ilizarov circular external fixator

BackgroundTiming the fixator removal is vital for a successful external fixation treatment. The purpose of this study was to determine the effectiveness of axial load–share ratio in vivo as a supplemental decision support tool for the safe removal of an Ilizarov circular external fixator.MethodsThis prospective observational study consists of 83 patients undergoing tibial or femoral lengthening with Ilizarov circular external fixation in our institution, from January 2011 to October 2019. In group I (38 patients), the external fixator was removed based on the surgeon’s clinical experience and radiographs from January 2011 to June 2015. In group II (45 patients), from July 2015 to October 2019, the supplemental axial load–share (LS) ratio test was accomplished without the knowledge of the clinical results by another medical team. The test was performed by electronically measuring forces in the fixator rods and in a ground force plate. When the LS ratio < 10% was consistent with the conclusion (dense bone formation was achieved in the distraction zone) drawn from the corresponding routine radiographs by the treating surgeon, the external fixator was removed.ResultsThere was no statistical significance in demographic data between the two groups (P > 0.05). In group I, 4 of the 38 patients suffered refracture (the refracture rate was 10.5%) after fixator removal, and bone union was finally achieved with further intervention by intramedullary nail. In group II, 36 patients terminated the external fixation after the first mechanical test, and another 9 patients terminated the external fixation at the subsequent test. None of the 45 patients in group II suffered refracture (the refracture rate was 0%). There was statistical significance in the refracture rate between the two groups (P < 0.05).ConclusionsAdequate assessment of bone regenerate is crucial before removing an external fixator to prevent deformation or refracture. The axial load–share ratio in vivo is a practically quantitative method to supplement radiography and clinical experience for the assessment of regenerate healing, and the axial load–share ratio dropped below 10% is a safe limit for the Ilizarov circular external fixator removal.

LLM-based collaborative programming: impact on students’ computational thinking and self-efficacy

At present, collaborative programming is a prevalent approach in programming education, yet its effectiveness often falls short due to the varying levels of coding skills among team members. To address these challenges, Large Language Models (LLMs) can be introduced as a supportive tool to enhance both the efficiency and outcomes of collaborative programming. In this shift, the structure of collaborative teams evolves from human-to-human to a new paradigm consisting of human, human, and AI. To investigate the effectiveness of integrating LLMs into collaborative programming, this study designed a quasi-experiment. To explore the effectiveness of integrating LLMs into collaborative programming, we conducted a quasi-experiment involving 82 sixth- and seventh-grade students, who were randomly assigned to either an experimental group or a control group. The results showed that incorporating LLMs into collaborative programming significantly reduced students’ cognitive load and improved their computational thinking skills. However, no significant difference in self-efficacy was observed between the two groups, likely due to the cognitive demand students faced when transitioning from graphical programming to text-based coding. Despite this, the study remains optimistic about the potential of LLM-enhanced collaborative programming, as students learning in this way exhibit lower cognitive load than those in conventional environments.

Supporting primary care clinicians in caring for patients with alcohol use disorder: study protocol for Records for Alcohol Care Enhancement (RACE), a factorial four-arm randomized trial

BackgroundUnhealthy alcohol use, a spectrum of use inclusive of risky consumption and alcohol use disorder (AUD), is a leading cause of preventable death in the United States. Most people with unhealthy alcohol use do not receive evidence-based treatment. This four-arm factorial design randomized trial will assess whether population health management (PHM) and clinical care management (CCM) support for primary care providers (PCPs) are associated with improved AUD treatment engagement among their patients, beyond electronic health record (EHR) prompting and decision support alone.MethodsPCPs from an urban safety-net hospital-based primary care clinic are randomized to one of four groups (1) EHR best practice advisory (BPA) and clinical decision support tools for unhealthy alcohol use (BPA), (2) BPA plus population health manager support, (3) BPA plus clinical care manager support, and (4) all three. All PCPs will have access to the EHR BPA and decision support tools which provide chart-based advisories and order set navigation. PCPs assigned to receive PHM support will receive quarterly panel-level feedback on AUD treatment metrics for their patients. PCPs assigned to receive CCM support will receive CCM facilitation of AUD treatment processes including medication counseling, referrals, and support through direct patient interactions. The primary outcome will be the percent of patients engaged in AUD treatment among those with a new AUD diagnosis on a PCP’s panel. Secondary outcomes include the percent of patients with a new diagnosis of AUD who (1) initiated AUD treatment, (2) were prescribed AUD medications within 90 days, and (3) numerical counts of a range of AUD health services (outpatient encounters, specialty AUD care encounters, referrals, and acute healthcare utilization) in this sample. We will assess the primary outcome and the acute healthcare utilization secondary outcomes using Medicaid claims; the remaining secondary outcomes will be assessed using EHR data.DiscussionThe study will evaluate how a targeted EHR innovation alone, compared with population health and care management enhancements alone or in combination, impact engagement in AUD treatment, a national quality of care measure. Findings will advance understanding of supports needed to improve systems of care for AUD in general settings.Trial registrationClinicalTrials.gov identifier/registration number (NCT number): NCT05492942

Sam–Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations

Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning of the SASH1-Sam1/EphA2-Sam complex is unknown, but EphA2 is a well-established and crucial player in cancer onset and progression. Thus, herein, to investigate a possible correlation between the formation of the SASH1-Sam1/EphA2-Sam complex and EphA2 activity in cancer, cancer-linked mutations in SASH1-Sam1 were deeply analyzed. Our research plan relied first on searching the COSMIC database for cancer-related SASH1 variants carrying missense mutations in the Sam1 domain and then, through a variety of bioinformatic tools and molecular dynamic simulations, studying how these mutations could affect the stability of SASH1-Sam1 alone, leading eventually to a defective fold. Next, through docking studies, with the support of AlphaFold2 structure predictions, we investigated if/how mutations in SASH1-Sam1 could affect binding to EphA2-Sam. Our study, apart from presenting a solid multistep research protocol to analyze structural consequences related to cancer-associated protein variants with the support of cutting-edge artificial intelligence tools, suggests a few mutations that could more likely modulate the interaction between SASH1-Sam1 and EphA2-Sam.

Predictive Models to Estimate Construction and Life Cycle Cost of Conventional and Prefabricated Bridges During Early Design Phases

State DOTs often need to decide during the early design phase whether to build their bridges using conventional or prefabricated construction methods based on overall cost-effectiveness. This often requires DOTs to develop reliable cost-estimates of these alternative construction methods with only limited available data during the early design phase. To support DOTs in this challenging task, this paper presents the development of a practical decision support tool that integrates novel bridge cost estimating models. These models were developed using stepwise, LASSO, and best subset regression techniques and a dataset of 241 conventional and prefabricated bridges. The developed LASSO model outperformed the two other models, achieving a mean absolute percentage error of 12.3% for conventional and 14.4% for prefabricated bridge projects. The developed DST and its cost-estimating models are expected to support bridge planners in identifying the most cost-effective bridge construction method during the early design phase.

Dexamethasone in critical coronavirus disease-2019 cases: Insights from a cross-sectional study

Objective: The objective of the study is to describe the clinical and laboratory characteristics of critically ill coronavirus disease-2019 (COVID-19) patients treated with dexamethasone in an intensive care unit (ICU) to provide a support tool for clinical decision-making. Design: A survey was conducted among hospitalized patients from November 2020 to March 2021, with data collected through patient interviews, medical records, and laboratory tests. Setting: This is a large hospital serving as a reference center for COVID-19 care in Bahia, Brazil. Patients: A convenience sample of 22 patients admitted to the COVID-19 ICU who signed informed consent to participate in the study. Methods: A cross-sectional study of patients admitted to the ICU with COVID-19. Data were analyzed using statistical methods. Results: The most common comorbidities among patients were hypertension (54%), diabetes (36%), and cardiovascular disease (27%). Among the deaths recorded, 55% of patients had hypertension, 44% had diabetes and/or required insulin therapy, 33% had a history of cardiovascular disease (including atrial fibrillation and congestive heart failure), and 22% had a history of stroke. Renal dysfunction (elevated creatinine); liver function abnormalities (increased alanine aminotransferase and aspartate aminotransferase); and elevated levels of ferritin, fibrinogen, and D-dimer were identified as potential indicators of disease progression. Among these factors, only elevated creatinine demonstrated a statistically significant association with an increased mortality risk. Conclusion: These findings provide a better understanding of the clinical course of severe acute respiratory syndrome coronavirus 2 infections and suggest that laboratory medicine is crucial in supporting clinical decision-making and advancing scientific and healthcare knowledge during the early phases of the COVID-19 pandemic. Relevance for patients: Identifying key risk factors, such as renal dysfunction, can improve early intervention and personalized treatment for critically ill COVID-19 patients.

Teachers’ perspective on the use of artificial intelligence on remote experimentation

Remote Laboratories have become crucial educational resources due to their implementation in institutions that have adopted hybrid teaching models. Moreover, artificial intelligence (AI) has increasingly been used to develop educational support tools. Remote Laboratories suffer from a limitation: when students conduct experimental activities, instructors cannot always be immediately available to resolve doubts. To address this limitation, a virtual assistant was integrated into the Acid–Base Titration II Remote Laboratory. The aim of this research is to understand the perspective of chemistry teachers from the Common Basic Cycle of the University of Buenos Aires on the use of this artificial intelligence tool. For this purpose, a focus group was conducted in which a series of questions were asked before and after using the AI tool. The findings reveal that teachers perceive great potential in the combination of these technologies. Furthermore, the virtual assistant could offer personalised assistance in real time, which ensures accompaniment during the completion of the Remote Laboratory.

Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System

Abstract Accurate CT protocol assignment is crucial for optimizing medical imaging procedures. The integration of large language models (LLMs) may be helpful, but its efficacy as a clinical decision support system for protocoling tasks remains unknown. This study aimed to develop and evaluate fine-tuned LLM specifically designed for CT protocoling, as well as assess its performance, both standalone and in concurrent use, in terms of effectiveness and efficiency within radiological workflows. This retrospective study included radiology tests for contrast-enhanced chest and abdominal CT examinations (2829/498/941 for training/validation/testing). Inputs involve the clinical indication section, age, and anatomic coverage. The LLM was fine-tuned for 15 epochs, selecting the best model by macro sensitivity in validation. Performance was then evaluated on 800 randomly selected cases from the test dataset. Two radiology residents and two radiologists assigned CT protocols with and without referencing the output of LLM to evaluate its efficacy as a clinical decision support system. The LLM exhibited high accuracy metrics, with top-1 and top-2 accuracies of 0.923 and 0.963, respectively, and a macro sensitivity of 0.907. It processed each case in an average of 0.39 s. The LLM, as a clinical decision support tool, improved accuracy both for residents (0.913 vs. 0.936) and radiologists (0.920 vs. 0.926 without and with LLM, respectively), with the improvement for residents being statistically significant (p = 0.02). Additionally, it reduced reading times by 14% for residents and 12% for radiologists. These results indicate the potential of LLMs to improve CT protocoling efficiency and diagnostic accuracy in radiological practice.

Benchmarking LLM chatbots’ oncological knowledge with the Turkish Society of Medical Oncology’s annual board examination questions

BackgroundLarge language models (LLMs) have shown promise in various medical applications, including clinical decision-making and education. In oncology, the increasing complexity of patient care and the vast volume of medical literature require efficient tools to assist practitioners. However, the use of LLMs in oncology education and knowledge assessment remains underexplored. This study aims to evaluate and compare the oncological knowledge of four LLMs using standardized board examination questions.MethodsWe assessed the performance of four LLMs—Claude 3.5 Sonnet (Anthropic), ChatGPT 4o (OpenAI), Llama-3 (Meta), and Gemini 1.5 (Google)—using the Turkish Society of Medical Oncology’s annual board examination questions from 2016 to 2024. A total of 790 valid multiple-choice questions covering various oncology topics were included. Each model was tested on its ability to answer these questions in Turkish. Performance was analyzed based on the number of correct answers, with statistical comparisons made using chi-square tests and one-way ANOVA.ResultsClaude 3.5 Sonnet outperformed the other models, passing all eight exams with an average score of 77.6%. ChatGPT 4o passed seven out of eight exams, with an average score of 67.8%. Llama-3 and Gemini 1.5 showed lower performance, passing four and three exams respectively, with average scores below 50%. Significant differences were observed among the models’ performances (F = 17.39, p < 0.001). Claude 3.5 and ChatGPT 4.0 demonstrated higher accuracy across most oncology topics. A decline in performance in recent years, particularly in the 2024 exam, suggests limitations due to outdated training data.ConclusionsSignificant differences in oncological knowledge were observed among the four LLMs, with Claude 3.5 Sonnet and ChatGPT 4o demonstrating superior performance. These findings suggest that advanced LLMs have the potential to serve as valuable tools in oncology education and decision support. However, regular updates and enhancements are necessary to maintain their relevance and accuracy, especially to incorporate the latest medical advancements.

Factors influencing paramedic conveyance decisions when attending children with minor head injury: a qualitative study

IntroductionChildren with head injury are commonly transported to the ED by ambulance. However, most of those conveyed are deemed non-serious and are discharged at triage. Research is needed to explore...

Addressing the "Black Hole" of Low Back Pain Care With Clinical Decision Support: User-Centered Design and Initial Usability Study.

Low back pain (LBP) is a highly prevalent problem causing substantial personal and societal burden. Although there are specific types of LBP, each with evidence-based treatment recommendations, most patients receive a nonspecific diagnosis that does not facilitate evidence-based and individualized care. We designed, developed, and initially tested the usability of a LBP diagnosis and treatment decision support tool based on the available evidence for use by clinicians who treat LBP, with an initial focus on chiropractic care. Our 3-step user-centered design approach consisted of identifying clinical requirements through the analysis of evidence reviews, iteratively identifying task-based user requirements and developing a working web-based prototype, and evaluating usability through scenario-based interviews and the System Usability Scale. The 5 participating users had an average of 18.5 years of practicing chiropractic medicine. Clinical requirements included 44 patient interview and examination items. Of these, 13 interview items were enabled for all patients and 13 were enabled conditional on other input items. One examination item was enabled for all patients and 16 were enabled conditional on other items. One item was a synthesis of interview and examination items. These items provided evidence of 12 possible working diagnoses of which 3 were macrodiagnoses and 9 were microdiagnoses. Each diagnosis had relevant treatment recommendations and corresponding patient educational materials. User requirements focused on tasks related to inputting data, and reviewing and selecting working diagnoses, treatments, and patient education. User input led to key refinements in the design, such as organizing the input questions by microdiagnosis, adding a patient summary screen that persists during data input and when reviewing output, adding more information buttons and graphics to input questions, and providing traceability by highlighting the input items used by the clinical logic to suggest a working diagnosis. Users believed that it would be important to have the tool accessible from within an electronic health record for adoption within their workflows. The System Usability Scale score for the prototype was 84.75 (range: 67.5-95), considered as the top 10th percentile. Users believed that the tool was easy to use although it would require training and practice on the clinical content to use it effectively. With such training and practice, users believed that it would improve care and shed light on the "black hole" of LBP diagnosis and treatment. Our systematic process of defining clinical requirements and eliciting user requirements to inform a clinician-facing decision support tool produced a prototype application that was viewed positively and with enthusiasm by clinical users. With further planned development, this tool has the potential to guide clinical evaluation, inform more specific diagnosis, and encourage patient education and individualized treatment planning for patients with LBP through the application of evidence at the point of care.

An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.

Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of routine diagnostic FFPE biopsy specimens (n = 55) representing controls, Reflux- (GERD), Eosinophilic-(EoE), Crohn's-(CD), Herpes simplex (HSV) and Candida (CA)-esophagitis by LC-MS/MS (DIA), we identified distinct signatures and functional networks (e.g. mitochondrial translation (EoE), immunoproteasome, complement and coagulations system (CD), ribosomal biogenesis (GERD)), and pathogen-specific proteins for HSV and CA. Moreover, combining these signatures with histological parameters in a machine learning model achieved high diagnostic accuracy (100% training set, 93.8% test set), and supported diagnostic decisions in borderline/challenging cases. Applied to a young patient representing a use case, the external GERD diagnosis could be revised to CD and ICAM1 was identified as highly abundant therapeutic target. This resulted in CyclosporinA as a personalized treatment recommendation by the local multidisciplinary molecular inflammation board. Our integrated AI-assisted morphoproteomic approach allows deeper insights in disease-specific molecular alterations and represents a promising tool in esophagitis-related precision medicine.

Co-designing to develop computational thinking skills in Nigeria K-12 using scratch

Abstract The need to integrate the teaching and learning of computational thinking (CT) in K-12 education has been on the rise since it was identified as a skill for solving 21st-century problems. The co-design pedagogical approach has shown great potential in promoting effective communication of CT to both university and K-12 students with the support of different educational tools in different contexts. To ensure Nigerian secondary school (K-12) students develop CT skills, a four-day co-design CT activities workshop was organized. Co-design pedagogy and constructivism theory were deployed in this study with students co-designing COVID-19 disease spread game for learning CT. A mixed method was adopted to investigate student’s interest, attitudes, understanding of CT, and their learning experience from implementing CT-based prototype using Scratch. This study recruited 40 students from two different secondary schools in Nigeria as participants. The result revealed that student’s interest in learning CT was aroused through the use of co-design pedagogy and Scratch (μ = 4.55, σ = 0.815). Similarly, students attitude toward CT after the intervention study shows positive (μ = 4.50, σ = 0.716). This study paved way for student’s skills development in teamwork and collaborative learning, communication, idea sharing, personal skill development, game design, and understanding of programming. This study instigates thinking ideation, inspires the application of CT concepts in daily life activities, and improves problem-solving skills. This study promotes and advocates for the application of co-design pedagogy to foster the teaching and learning of CT in a Nigerian context. This study contributes to knowledge by promoting the use of Scratch as a tool for co-designing in learning CT, proposing a four-phase co-design application flow for the integration of co-design pedagogy with Scratch for learning CT in the Nigerian K-12 context and suggesting ways to implement the teaching and learning of CT in K-12 education.

Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.

Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a significant ongoing challenge in the management of geriatric TBI. As the population ages and co-existing medical conditions complexify, so does the need to improve the quality of care for this population. Utilizing early hospital admission variables, this study will create and validate a multinomial decision tree that predicts the discharge disposition of older patients with fall-related TBI. From the National Trauma Data Bank, we retrospectively analyzed 11,977 older patients with a fall-related TBI (2017-2021). Clinical variables included Glasgow Coma Scale (GCS) score, intracranial pressure monitor use, venous thromboembolism (VTE) prophylaxis, and initial vital signs. Outcomes included hospital discharge disposition re-categorized into home, care facility, or deceased. Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B = 100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots. Our decision tree used seven admission variables to predict the discharge disposition of older TBI patients. Significant non-modifiable variables included total GCS and injury severity scores, while VTE prophylaxis type was the most important interventional variable. Patients who did not receive VTE prophylaxis treatment had a higher probability of death. The predictive performance of the tree in terms of AUC value (95% confidence intervals) in the training cohort for death, care, and home were 0.66 (0.65-0.67), 0.75 (0.73-0.76), and 0.77 (0.76-0.79), respectively. In the test cohort, the values were 0.64 (0.62-0.67), 0.75 (0.72-0.77), and 0.77 (0.73-0.79). We have developed and internally validated a multinomial decision tree to predict the discharge destination of older patients with TBI. This tree could serve as a decision support tool for caregivers to manage older patients better and inform decision-making. However, the tree must be externally validated using prospective data to ascertain its predictive and clinical importance.

Trauma patients' perspectives on the needs, understanding and usefulness of personalized predictions for life after injury: a qualitative interview study using thematic analysis.

To explore trauma patients' perspectives on the need, understanding and usefulness of personalized predictions after injury to assist in rehabilitation. We performed semi-structured online interviews. Participants (N = 30 trauma patients, admitted to the hospital for an injury in the past 5 years) were exposed to a support tool that provides personalized predictions on recovery after injury. Interview data were transcribed verbatim and thematically analyzed. Four themes were identified. Patients expressed (1) a need for personal information and felt that information about recovery was lacking. The most important (2) reasons for needing to receive personalized predictions were reassurance and managing expectations. However, (3) understanding the prediction model was challenging. Patients expressed receiving relatively poor predictions would not undermine (4) usefulness in practice, as they would rather know about what life has in store for them so they can prepare for life after injury better. Trauma patients have a need for receiving personalized predictions as they perceive them to be a useful addition to clinical practice. Understanding such predictions might be challenging, so more time should be spent on how these can be communicated.

Perceptions of effectiveness and ethical use of AI tools in academic writing: A study Among PhD scholars in India

This study explores the perceptions, effectiveness, and ethical implications of AI tools in academic writing among PhD scholars in India. It evaluates the comparative effectiveness of AI tools versus traditional methods for paraphrasing, plagiarism detection, and citation improvement. The research also investigates user confidence in AI tools’ adaptability to various academic disciplines and their potential to support critical thinking and academic integrity. A structured quantitative approach was employed, utilizing validated surveys with 184 respondents from the University of Mumbai. Findings indicate that while AI tools enhance clarity, flow, and efficiency, they raise concerns about over-reliance, originality, and ethical transparency. Advanced-year scholars valued citation improvements but were critical of originality-related functions. The study emphasizes responsible AI use, advocating for its role as a complementary, supportive tool to foster creativity and intellectual rigor. These findings contribute to the ongoing discourse on integrating AI responsibly into academic practices.

Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, the possibility of an effective prediction of the direction of future changes in the price of crude oil is especially significant. Most existing works focus on the analysis of daily closing prices. This kind of approach results, on the one hand, in losing important information about the dynamics of changes during the day. On the other hand, it does not allow for the modelling of short-term price changes that are especially important in cases of financial derivatives having crude oil as their base instrument. The goal of the following article is the analysis of possible applications of a binary–temporal representation in the modelling and construction of effective decision support systems on the crude oil market. The analysis encompasses all researched state models, e.g., those applying mean and trend analysis. Also, the selection of parameters was optimized for Brent crude oil rates. The presented research confirms the high effectiveness of our state modelling system in predicting oil prices on a level that allows for the construction of financially effective investment decision support systems. The obtained results were verified based on proper backtests from different quotation periods. The presented results can be used both in scientific analyses and in the construction of investment support tools for the crude oil market.

Smart, Sustainable, Resilient, and Inclusive Cities: Integrating Performance Assessment Indicators into an Ontology-Oriented Scheme in Support of the Urban Planning Practice

The unprecedented pace of urbanization has been exerting significant strain on cities, raising critical concerns across environmental, economic, social, and technological aspects. In response, the ‘Smart City’ concept has emerged as a novel urban development paradigm, aiming at addressing contemporary urban issues, enhancing cities’ competitiveness and prosperity, and fostering active participation through the strategic utilization of state-of-the-art technologies. However, the smart city term suffers from considerable conceptual ambiguity, thereby provoking intense confusion and misunderstanding among interested parties and leading to the implementation of ineffective initiatives. Moreover, the priorities of sustainability, resilience, and inclusiveness have gained prominence in the urban planning discourse, necessitating a more integrated view that aligns urban targets with performance assessment across various domains. In light of these issues, this study endeavors to clarify the above-mentioned conceptual vagueness by developing a holistic, indicator-oriented smart city ontology. The proposed knowledge representation scheme is intended to serve as a Decision Support Tool that will facilitate policymakers to tackle urban challenges and formulate sound policies. Additionally, it is expected to contribute to the fields of spatial and developmental planning by establishing a standardized framework for assessing and monitoring cities’ performance, while elucidating the complex interrelationships and trade-offs among diverse urban dimensions.

CDK4/6 inhibitor plus endocrine therapy for advanced breast cancer: results from a web-based survey in Japan.

Though efforts have been made toward standardizing access to quality cancer care in Japan, there are still geographical and institutional disparities in the level of cancer care availability. We investigated the utilization of cyclin-dependent kinase 4/6 inhibitors plus endocrine therapy (CDK4/6i+ET) as first-line (1 L) treatment for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) in Japan. This cross-sectional survey included physicians who had treated ≥3 1 L patients with HR+/HER2- ABC in the past year. Of 41,695 physicians invited, 300 were included in the analysis. The mean percentage share of CDK4/6i+ET and ET monotherapy was 38.3% and 42.2%, respectively. Common challenges facing CDK4/6i+ET prescription were adverse reaction management, prohibitive cost, and a preference for ET monotherapy for treating elderly patients. Key solutions included reducing the burden of adverse reaction management, improving financial support, and preparing educational videos for medical staff. The study concluded that CDK4/6i+ET is not well established as a 1 L option in Japan as of 2022. More effective ways of creating awareness and supportive tools are needed for CDK4/6i+ET to be adopted as standard of care in Japan. UMIN000050760.