Assessment Of Capabilities Research Articles

Assessment of Disaster Preparedness and Response Capability Among Paramedics in Arar City, KSA: A Cross-Sectional Study

Objective: This study aimed to assess the level of disaster preparedness and response capability among paramedics in Arar City, Kingdom of Saudi Arabia (KSA). Methods: A cross-sectional survey was conducted among a random sample of 120 paramedics from 3 hospitals in Arar City. The Disaster Preparedness Evaluation Tool (DPET), a validated 36-item questionnaire, was used to measure knowledge, skills, and attitudes related to disaster preparedness and response. Descriptive and inferential statistics were computed. Results: The response rate was 91.7% (n=110). The mean overall DPET score was 3.47±0.74 out of 5, indicating moderate to high preparedness. Paramedics scored highest on knowledge of triage and evacuation (4.12±0.85), and lowest on familiarity with the hospital's disaster plan (2.86±1.09). Years of experience, previous disaster response participation, and recent training were significantly associated with higher preparedness scores (p<0.05). Key perceived barriers included lack of simulations, inadequate equipment, and poor multi-agency coordination. Conclusions: Paramedics in Arar City reported moderate to high levels of disaster preparedness. However, specific areas for improvement were identified. Findings highlight the importance of regular training, drills, and interprofessional collaboration to enhance paramedics' readiness for effective disaster response. Insights from this study can inform the development of targeted interventions and policies to strengthen the resilience of local EMS systems.

A Framework for the Multi-Dimensional Assessment of Interoperability for Open Data Ecosystems Development

The generation of social and economic value from the vast quantities of data opened by governments has been much lower than the initial expectations. It requires the development and cooperation of ecosystems composed of various public, private, and non-profit actors. For the development of these ecosystems in order to generate social and economic value from open government data (OGD), necessary conditions are, on the one hand, the availability of a critical mass of OGD and, on the other hand, the existence of some level of homogeneity and interoperability among the numerous portals and websites providing them, which enables their combined exploitation that increases the potential for value generation from them. This paper focuses on the latter and develops the Technical, Semantic, Legal, and Organizational (TSLO) Interoperability Framework, a conceptual tool designed to provide a structured multi-dimensional assessment of the interoperability capabilities of OGD infrastructures, based on the ‘European Interoperability Framework’ (EIF) as well as relevant OGD literature. This TSLO framework has been applied for the assessment of the above main dimensions of the interoperability of the OGD infrastructures of the Greek local government administration institutions of all three layers (decentralized, regional, and municipal). After this application, nine interviews were conducted with nine municipalities that do not provide OGD in order to gain a deeper understanding of the reasons for this. The results of this first application of TSLO revealed not only the low participation of Greek local government institutions, and especially of the municipalities, in the OGD movement (with only 8 out of the 332 municipalities providing open data on their portals or websites), but also the inconsistent implementation of this, especially with respect to the use of semantic technologies as well as the legal frameworks under which the open data are provided, resulting in low levels of interoperability among them. The interviews that were conducted revealed that the opening of some of their data was not a priority for them, as their limited resources had to be used for the development of internal information systems for increasing their efficiency, as well as e-services for the citizens, enabling them to conduct transactions electronically (through the Internet). Our study makes a contribution to the still limited body of research on OGD ecosystems, examining an important precondition for their development that has not been researched in previous literature; also, our framework can contribute to the increase of interoperability of OGD infrastructures and therefore to the increase of social and economic value generations for the OGD they provide.

Envisioning the incorporation of Generative Artificial Intelligence into future product design education: Insights from practitioners, educators, and students

The advancement of Generative Artificial Intelligence (GenAI) has introduced diverse opportunities and challenges in product design, demanding a corresponding evolution in design education. This study investigated GenAI’s roles in product design and its implications for design education through semi-structured interviews with 24 participants, comprising practitioners, educators, and students, who had hands-on experience leveraging GenAI in design. Through thematic analysis, we identified critical functions of GenAI in product design, including generating inspiration, exploring potential solutions, conducting prototyping, performing evaluations, finalizing detailed outputs, and serving as design materials, presenting both opportunities and challenges. Moreover, fully incorporating GenAI in product design requires adaptation in educational initiatives, including capability development, curriculum content, and assessment methods. This study provides insights from a multi-stakeholder perspective, revealing the roles of GenAI in product design and advancing educational curricula to prepare the next-generation designers to be proficient in effectively leveraging GenAI for innovation.

Using Big Data in Labor Market Analysis: Theoretical Approaches and Methodological Tools

The article considers the possibility of using big data technologies and large language models to analyze the labor market in the Republic of Belarus. Theoretical approaches using big data have been developed, which implies determining both the possibility of conducting labor market analytics using online sources and effective tools for collecting and processing information on the labor market from online sources. The use of big data and large language models will improve the quality and accuracy of labor market analysis in the republic, and the use of advanced analytical tools will provide a more complete and detailed understanding of the labor market dynamics. The study is based on the analysis of existing theoretical approaches, the practice of using big data and large language models in foreign countries, as well as an assessment of the current capabilities and limitations of using these technologies in Belarus. Machine learning, big data analysis and modeling were used as tools. The results of the study can be used to improve labor market management strategies, as well as to develop employment policies and programs focused on modern challenges and opportunities of the digital economy.

Review of thermal neutron scintillators: Evaluation metrics and future prospects for demanding applications

Neutron applications are progressing rapidly, requiring detectors to meet increasingly rigorous criteria. Specifications such as high neutron counting rates, fast timing resolution and low background have a significant impact on choice of scintillator and how scintillators are evaluated. This work considers detector requirements for neutron scattering instruments that are becoming more stringent as the sources and scattering technologies continue to improve. Meanwhile, both prominent scintillators used for neutron scattering detectors, ZnS:Ag/6LiF and GS-glass, remain stagnant. While these scintillators have been suitable for many years, they are becoming a limiting factor for neutron scattering instruments. ZnS:Ag is inhibiting count rate capability and GS-glass is too gamma sensitive so alternatives need to be found. A brief explanation of neutron scattering, its detector requirements and how those relate to scintillator properties will be discussed. Furthermore, this work explains how standard characterization methods and reporting of scintillation properties are not entirely suitable for high count rate, low background applications like neutron scattering. As a result, modifications to characterization of gamma discrimination, light output, and decay kinetics are suggested. Additional steps are suggested including neutron activation and a more appropriate assessment of count rate capability. A non-exhaustive set of 6Li containing neutron sensitive scintillators including ZnS:Ag/6LiF, GS20 glass, ZnO:Zn/6LiF, Cs6LiYCl:Ce, 6LiI:Eu, 6LiI:Ce, 6LiCaAlF:Eu, LiCaAlF:Ce, transparent rubber sheet (TRUST) 6LiCaAlF:Eu, TRUST 6LiCaAlF:Eu with added fluorophore and EJ-270 (6Li containing plastic) are reviewed and studied from this different perspective. Finally, new scintillator developments relevant to high count rate, low background applications are discussed.

Performance Evaluation Methods and Suggestions for High-Voltage Generator and Tube of DR System

In the design of DR systems, high-voltage generators and X-ray tubes are two key components whose performance directly affects the system's exposure capabilities. This article first explains why it is necessary to conduct an in-depth evaluation of the performance of these two parts during the design of DR products. Then, it provides a detailed description of the evaluation methods, including the assessment of single exposure capability and continuous exposure capability. Finally, the article offers some suggestions and measures to enhance continuous exposure capability. These methods and suggestions not only help in selecting appropriate key components but also provide an important reference for the design, integration, and testing of DR systems.

Molecular Design of Phthalocyanine-Based Drug Coassembly with Tailored Function.

Coassemblies with tailored functions, such as drug loading, tissue targeting and releasing, therapeutic and/or imaging purposes, and so on, have been widely studied and applied in biomedicine. De novo design of these coassemblies hinges on an integrated approach involving synergy between various design strategies, ranging from structure screening of combinations of "phthalocyanine-chemotherapeutic drug" molecules for molecular scaffolds, exploration of related fabrication principles to verification of intended activity of specific designs. Here, we propose an integrated approach combining computation and experiments to design from scratch coassembled nanoparticles. This nanocoassembly, termed NanoPC here, consists of phthalocyanine-based scaffolds hosting chemotherapeutic drugs, aimed at hypersensitive chemotherapy guided by photoimaging for targeting tumors. Our design starts from the selection of phthalocyanine derivatives that are not aggregation-prone, followed by computational screening of coassembled molecules covering various categories of chemotherapy drugs. To facilitate an efficient and accurate assessment of coassembly capabilities, we utilize small systems as surrogates to enable free-energy calculations at all-atom levels facilitated with enhanced sampling and statistical mechanics for efficient and accurate evaluation of coassembly ability. The final top NanoPC candidate, comprised of phthalocyanine PcL and cytarabine (CYT), can greatly increase the fluorescence intensity ratio of tumor/liver by 21.5 times and achieve higher antitumor efficiency in a pH-dependent manner. Therefore, the designing approach proposed here has a potential pattern, which can provide ideas and references for the design and development of coassembled nanodrugs with tailored functions and applications in biomedicine.

Solid-State Gas Sensors with Ni-Based Sensing Materials for Highly Selective Detecting NOx.

Precise monitoring of NOx concentrations in nitric oxide delivery systems is crucial to ensure the safety and well-being of patients undergoing inhaled nitric oxide (iNO) therapy for pulmonary arterial hypertension. Currently, NOx sensing in commercialized iNO instruments predominantly relies on chemiluminescence sensors, which not only drives up costs but also limits their portability. Herein, we developed solid-state gas sensors utilizing Ni-based sensing materials for effectively tracking the levels of NO and NO2 in the NO delivery system. These sensors comprised of NiO-SE or (NiFe2O4 + 30 wt.% Fe2O3)-SE vs. Mn-based RE demonstrated high selectivity toward 100 ppm NO under the interference of 10 ppm NO2 or 3 ppm NO2 under the interference of 100 ppm NO, respectively. Meanwhile, excellent stability, repeatability, and humidity resistance were also verified for the proposed sensors. Sensing mechanisms were thoroughly investigated through assessments of adsorption capabilities and electrochemical reactivity. It turns out that the superior electrochemical reactivity of NiO toward NO, alongside the NO2 favorable adsorption characteristics of (NiFe2O4 + 30 wt.% Fe2O3), is the primary reason for the high selectivity to NOx. These findings indicate a bright future for the application of these NOx sensors in innovative iNO treatment technologies.

Enhancing teamwork in higher education: Experiences of podiatry students using SPARKPLUS for self- and peer-assessment in group work.

Teamwork is essential for delivering high-quality healthcare, particularly given the increasing complexity of care due to chronic diseases, comorbidities and limited resources. The necessary skills and attributes for effective teamwork are often taught and assessed through group work within healthcare education programs. While group work can assist the development of skills and attributes of students to be effective team members, it also presents challenges, such as ensuring equitable student contributions. The Self and Peer Assessment Resource Kit (SPARKPLUS) endeavours to increase transparency, address inequities and provide learning-oriented feedback in group work. However, there is limited evidence on its utility within university health education. This study was conducted with the aim of evaluating the experiences of podiatry students who used SPARKPLUS for self-evaluation and peer review during group work. Undergraduate podiatry students (n=102) enrolled in a fourth-year (final year) project-based subject provided self- and peer-assessment ratings for two group assessment tasks using SPARKPLUS. Eleven students (10.8%) participated in semi-structured focus groups, which were audio recorded and transcribed verbatim. Inductive thematic analysis was conducted by two independent reviewers. Four overarching themes and 11 subthemes were identified: (i) Performance (1a. Equity; 1b. Accountability), (ii) Peer inter-relationships (2a. Social dynamics; 2b. Fear of consequences), (iii) Feedback and reflection (3a. Self-reflection; 3b. Receiving and responding to feedback; 3c. Supervisor action; 3d. Avoiding confrontation) and (iv) Utility (4a. Enablers; 4b. Barriers; 4c. Integration throughout course). Overall, SPARKPLUS encouraged equity, accountability and self-reflection among group members. There was perceived risk of negative group dynamics and relationships when poor feedback was given. It was equivocal if team member contributions increased. Self- and peer-assessment with SPARKPLUS is a useful approach towards addressing inequities in group work within health education and may offer insight into the development and assessment of teamwork capabilities.

Investigation of coarse-graining parameters for super-grid LEM closure applied to LES of practical bluff-body flames

Large Eddy Simulation (LES) coupled with the Linear Eddy Model (LEM) provides a robust method for studying turbulent combustion, but it is computationally expensive due to the need for highly resolved sub-grid LEM domains. These domains simulate sub-grid stirring through stochastic rearrangements of scalar fields, while large-scale transport is modelled using a Lagrangian ‘splicing’ scheme. To address the computational cost of LES-LEM, a super-grid (SG) framework for LEM closure was developed by the authors (Comb. Theor. Model. 28, 2024), which uses coarse-graining, on-the-fly chemistry tabulation and a presumed PDF approach to reconstruct thermochemical fields at LES resolution. This study applies SG-LEM to a challenging setup, Case 1 of the Volvo Validation Rig, which involves a bluff-body-stabilised turbulent premixed propane-air flame, as a stress test to identify limitations that were not revealed by the previous application, in particular that of the coarse-graining parameters used to generate the super-grid. The intent is to yield a more realistically constrained assessment of the current capabilities of the method, and insight into possible ways for improving it. Four simulations were conducted using three SG cluster sizes. The finest resolution was tested with a global 2-step mechanism, showing good agreement with experimental data for temperature and velocity, particularly near the bluff body. The two larger cluster sizes used a 66-step skeletal mechanism for more detailed chemical closure but led to unphysical quenching due to splicing inaccuracies. To mitigate these issues, two novel additions were introduced: an intra-cluster-stirring routine and a method to control SG cluster shapes to reduce numerical dissipation. These methods improved flame stability with coarser SG clusters and more detailed mechanisms. Comparison with experiments showed good agreement for temperature and velocity, though elevated CO levels were observed in the recirculation region. Potential methods for further improving SG-LEM's capabilities are discussed.

Enhancing the electrochemical characteristics of P2-Na0.7Mn0.6Cu0.3Cr0.1O2 cathode materials through morphological manipulation: assessment of rate capability and coulombic efficiency

Abstract Manganese-based layered oxides are regarded as an excellent cathode material for Na-ion batteries. These materials are susceptible to phase transitions, which result in structural instability and constrain their reversible capacity. Layered self-assembled microsphere-type cathodes Na0.7Mn0.6Cu0.3Cr0.1O2 were developed to modify the efficiency of sodium-ion batteries through a technique focused on controlling morphology. The incorporation of organic compounds such as benzoic acid employs molecular design techniques to modify intermolecular interactions and increase the spacing between layers. The self-assembled layered spherical architecture enhances the interface between the cathode and electrolyte, thereby markedly boosting the transport efficiency of sodium ions. Based on the cathode for batteries, it demonstrates a substantial initial capacity of 200.26 mAh g-1 at a current density of 26 mA g-1 within the voltage amount of 1.5-4.1 V, with a capacity retention of 82.69% after 100 cycles.

Supporting diversity in clinical trials: the equitable breakthroughs in medicine site maturity model

BackgroundAmong the most powerful barriers to broader inclusion of diverse participants in clinical trials are social determinants of health, trustworthiness of health care providers and research institutions, and competing pressures on potential participants. Nevertheless, current tools to assess organizational capabilities for clinical trial diversity focus primarily on trial infrastructure, rely solely on quantitative self-reported data, and lack meaningful assessment of capabilities related to community engagement.MethodsThe Equitable Breakthroughs in Medicine (EQBMED) initiative developed a holistic, collaborative, site-driven formative model and accompanying assessment to catalog sites’ current capabilities and identify opportunities for growth in both conducting industry-sponsored clinical trials and enriching diversity of those trials. The model builds upon prior work and reflects unification of two historically distinct components—research operations and community engagement—since sustainable clinical trial diversity efforts must overcome these silos. Here we present the methodology we used to develop the model and accompanying assessment, describe how findings can support clinical trial diversity efforts, and report findings from early field testing at three U.S. sites.ResultsThe first three sites were diverse in size (e.g., < 250–1 K beds), with varying levels of clinical trial capabilities and community engagement. The maturity assessment laid the foundation for sites to identify and prioritize key areas to advance clinical trial diversity capabilities, and each has made tangible progress. In parallel to completing the assessment with these early sites to understand their maturity and set actionable goals, we also collected their feedback on content validity (e.g., clarity, comprehensiveness, terminology) and feasibility (e.g., ability to collect needed information and data, time required). We describe refinements made to improve the assessment and streamline the process. The EQBMED program will deploy the assessment across various site types (e.g., FQHCs, safety net hospitals) and make further refinements as warranted.ConclusionsStrategic investment in clinical trial diversity requires structured assessment of site maturity as a starting point for collaborative action. We propose the EQBMED maturity model as a first step toward informing efforts to increase representation of diverse populations in clinical research.

Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction-associated steatotic liver disease (MASLD). We included individuals from NHANES 2017-2018. The validity and reliability of MASLD diagnosis by GPT-3.5 and GPT-4 were quantitatively examined and compared with those of the Fatty Liver Index (FLI) and United States FLI (USFLI). A receiver operating characteristic curve was conducted to assess the accuracy of MASLD diagnosis via different scoring systems. Additionally, GPT-4V's potential in clinical diagnosis using ultrasound images from MASLD patients was evaluated to provide assessments of LLM capabilities in both textual and visual data interpretation. GPT-4 demonstrated comparable performance in MASLD diagnosis to FLI and USFLI with the AUROC values of .831 (95% CI .796-.867), .817 (95% CI .797-.837) and .827 (95% CI .807-.848), respectively. GPT-4 exhibited a trend of enhanced accuracy, clinical relevance and efficiency compared to GPT-3.5 based on clinician evaluation. Additionally, Pearson's r values between GPT-4 and FLI, as well as USFLI, were .718 and .695, respectively, indicating robust and moderate correlations. Moreover, GPT-4V showed potential in understanding characteristics from hepatic ultrasound imaging but exhibited limited interpretive accuracy in diagnosing MASLD compared to skilled radiologists. GPT-4 achieved performance comparable to traditional risk scores in diagnosing MASLD and exhibited improved convenience, versatility and the capacity to offer user-friendly outputs. The integration of GPT-4V highlights the capacities of LLMs in handling both textual and visual medical data, reinforcing their expansive utility in healthcare practice.

BIM capability assessment for improving the efficiency of design in railway projects

The construction industry is increasingly adopting Building Information Modelling (BIM) due to its benefits, including early collaboration, error reduction, and improved quality. This study systematically assesses BIM capabilities in the design processes of railway projects managed by Network Rail (NR), a national authority in the UK, and Gaziantep Metropolitan Municipality (GMM), a local authority in Turkey. Using a tailored BIM capability assessment tool, the study evaluates the performance of BIM attributes across architectural, structural, and building services design. The findings indicate that NR achieved Integrated BIM (Level 2) across all design disciplines driven by enhanced BIM uses such as code and compliance checking and phase and 4D planning. In contrast, GMM achieved Performed BIM (Level 1), particularly in structural design where BIM is applied selectively for tasks like design authoring and 3D coordination. The difference in scale leads to varying BIM implementations, with NR benefiting from a standardised approach supported by national-level mandates and resources. The study highlights that smaller authorities like GMM can improve BIM practices by adopting national-level strategies. Furthermore, organisations in countries with mandatory BIM requirements show more advanced BIM applications, such as integrating BIM with GIS for clash detection and improved coordination.

Variable droop gain frequency supporting control with maximum rotor kinetic energy utilization for wind-storage system

To address the emerging frequency stability issue brought by the large replacement of synchronous generators with renewable generations, wind turbine generators are required to possess frequency-supporting capability. However, existing frequency-supporting control strategies lack the assessment of the frequency support capability of wind turbine generators, leading to degraded control performance under various situations. Aiming to solve this problem, this paper proposes a variable-droop-gain control for wind turbine generators with maximum rotor kinetic energy utilization. Firstly, an analytical relationship was established between droop gain, disturbance scale, and rotor speed. Subsequently, the released energy of the wind turbine generator is evaluated, which equals the difference in the rotor kinetic energy under the initial and the post-disturbance steady-state rotor speed. It was proved that the released kinetic energy cannot exceed a certain proportion of total rotor kinetic energy. Accordingly, a variable initial gain scheme is proposed, which determines the initial droop gain as per the disturbance scale for maximizing the kinetic energy release of wind turbines. Moreover, an additional real-time droop gain adjustment rule is added to prevent the over-deceleration of wind turbines. The simulation results show that the proposed scheme may provide the maximum KE release and effectively improve the system frequency nadir while ensuring the safe operation of wind turbine generators.

Construction and application of the emergency resilience capability evaluation scale and model for grassroots medical workers: a cross-sectional study in Guangzhou, China.

Public health emergencies pose a serious challenge to the ability of medical personnel to respond. We aimed to establish a scientific and effective resilience capability assessment scale for grassroots medical workers and explore the way to improve resilience. Developed a resilience ability assessment scale and applied it on the medical personnel who worked in community health service centers for more than 1 year. Related indicators were identified through literature review and the Delphi method. The established scale was given weight using the attribute analytic hierarchy process, and the evaluation model was built through the grey correlation analysis. A three-level indicator scale was established, which included three first-level indicators [professional quality (weight 0.346), psychological capital (weight 0.614), and emergency attitude (weight 0.040)]. The second-level indicators had 13 contents and were further subdivided into 46 three-level indicators. The overall resilience ability assessment score of 347 medical workers in Guangzhou was only 0.787. However, steady scientific-exercise habits and relative training can help improve their public health emergency resilience ability (p < 0.05). The establishment of the scale can provide a strategic basis for improving the resilience strength of grass-roots medical personnel. We should encourage this group to form a good habit of exercise, strengthen their crisis awareness by actively organizing theoretical training and practical exercises on public health emergencies, and improve their psychological capital level. In this study, 347 medical workers from 6 basic medical institutions in Guangzhou participated in the questionnaire.

Synthesis and Binding Properties of High-Affinity Histidine-Bearing Polymers for Wood Lignin.

The pursuit of molecules capable of binding to wood lignin is pivotal for advancing lignin degradation technology, particularly when combined with lignin degradation catalysts. In this study, synthetic polymers bearing histidine moieties, demonstrating remarkable affinity for wood lignin are reported. These polymers, featuring varying degrees of histidine substitution in the form of histidine methyl esters, are synthesized through controlled radical polymerization of an activated ester-bearing monomer, employing a fluorescein-labeled chain transfer agent and subsequent postpolymerization amidation with histidine methyl ester. The binding properties of these histidine-bearing polymers with milled wood lignin under aqueous conditions are investigated. Qualitative assessment of lignin-binding capabilities involve spectroscopic analysis of changes in absorbance of visible light and fluorescence intensity. Furthermore, quantitative evaluation is conducted through surface plasmon resonance measurements to determine the binding parameters of the polymers with wood lignin. Notably, polymers with higher histidine substitution exhibit enhanced binding affinity compared to those with lower histidine substitution levels.

Toroidal modified Miller-Turner CME model in EUHFORIA: Validation and comparison with flux rope and spheromak

Context. Rising concerns about the impact of space-weather-related disruptions demand modelling and reliable forecasting of coronal mass ejection (CME) impacts. Aims. In this study, we demonstrate the application of the modified Miller-Turner (mMT) model implemented within EUropean Heliospheric FORecasting Information Asset (EUHFORIA) in forecasting the geo-effectiveness of observed coronal mass ejection (CME) events in the heliosphere. Our goal is to develop a model that not only has a global geometry, in order to improve overall forecasting, but is also fast enough for operational space-weather forecasting. Methods. We test the original full torus implementation and introduce a new three-fourths Torus version called the Horseshoe CME model. This new model has a more realistic CME geometry, and overcomes the inaccuracies of the full torus geometry. We constrain the torus geometrical and magnetic field parameters using observed signatures of the CMEs before, during, and after the eruption. We perform EUHFORIA simulations for two validation cases – the isolated CME event of 12 July 2012 and the CME–CME interaction event of 8–10 September 2014. We performed an assessment of the model’s capability to predict the most important Bz component using the advanced dynamic time-warping (DTW) technique. Results. The Horseshoe model predictions of CME arrival time and geo-effectiveness for both validation events compare well with the observations and are weighed against the results obtained with the spheromak and FRi3D models, which were already available in EUHFORIA. Conclusions. The runtime of the Horseshoe model simulations is close to that of the spheromak model, which is suitable for operational space weather forecasting. However, the capability of the magnetic field prediction at 1 AU of the Horseshoe model is close to that of the FRi3D model. In addition, we demonstrate that the Horseshoe CME model can be used for simulating successive CMEs in EUHFORIA, overcoming a limitation of the FRi3D model.

Toward Foundation Models in Radiology? Quantitative Assessment of GPT-4V's Multimodal and Multianatomic Region Capabilities.

Background Large language models have already demonstrated potential in medical text processing. GPT-4V, a large vision-language model from OpenAI, has shown potential for medical imaging, yet a quantitative analysis is lacking. Purpose To quantitatively assess the performance of GPT-4V in interpreting radiologic images using unseen data. Materials and Methods This retrospective study included single representative abnormal and healthy control images from neuroradiology, cardiothoracic radiology, and musculoskeletal radiology (CT, MRI, radiography) to generate reports using GPT-4V via the application programming interface from February to March 2024. The factual correctness of free-text reports and the performance in detecting abnormalities in binary classification tasks were assessed using accuracy, sensitivity, and specificity. The binary classification performance was compared with that of a first-year nonradiologist in training and four board-certified radiologists. Results A total of 515 images in 470 patients (median age, 61 years [IQR, 44-71 years]; 267 male) were included, of which 345 images were abnormal. GPT-4V correctly identified the imaging modality and anatomic region in 100% (515 of 515) and 99.2% (511 of 515) of images, respectively. Diagnostic accuracy in free-text reports was between 0% (0 of 33 images) for pneumothorax (CT and radiography) and 90% (45 of 50 images) for brain tumor (MRI). In binary classification tasks, GPT-4V showed sensitivities between 56% (14 of 25 images) for ischemic stroke and 100% (25 of 25 images) for brain hemorrhage and specificities between 8% (two of 25 images) for brain hemorrhage and 52% (13 of 25 images) for pneumothorax, compared with a pooled sensitivity of 97.2% (1103 of 1135 images) and pooled specificity of 97.2% (1084 of 1115 images) for the human readers across all tasks. The model exhibited a clear tendency to overdiagnose abnormalities, with 86.5% (147 of 170 images) and 67.7% (151 of 223 images) false-positive rates for the free-text and binary classification tasks, respectively. Conclusion GPT-4V, in its earliest version, recognized medical image content and reliably determined the modality and anatomic region from single images. However, GPT-4V failed to detect, classify, or rule out abnormalities in image interpretation. © RSNA, 2024 Supplemental material is available for this article.

The Effect of Ischemic Preconditioning on Tennis Exercise Performance and the Recovery Subsequent to a Simulated Tennis Match: A Randomized Controlled Trial.

The purpose of this study was to investigate the effects of acute ischemic preconditioning (IPC) on tennis skill and physical exercise performance, as well as to explore whether 7-day repeated IPC (RIPC) accelerated fatigue recovery after a simulated tennis match. Twenty-nine male tennis-specific current students were randomly allocated into 1 of 2 groups: SHAM (n = 14, 3 × 5min at 20mm Hg) and IPC (n = 15, 3 × 5min at 220mm Hg). Participants in both groups engaged in acute IPC and RIPC interventions. After the first acute IPC intervention, assessments were conducted to evaluate tennis-specific skills and overall physical exercise capacity. Following completion of chronic RIPC interventions, all participants competed in a simulated tennis match specifically designed to induce fatigue. To evaluate recovery from this induced fatigue, physical exercise capacity tests were conducted at 24 and 48hours postmatch, allowing for an assessment of the participants' recovery capabilities over time. After the first acute intervention, notable differences were observed between the IPC and SHAM groups in their performance on the repeated-sprint ability test. Specifically, the total times recorded were significantly shorter in the IPC group compared with the SHAM group (IPC: 109.05 [2.70] vs SHAM: 114.57 [7.45]s, P = .012), and this trend was also reflected in their best times (IPC: 4.20 [0.18]s vs SHAM: 4.39 [0.30]s, P = .042), indicating an immediate benefit of the IPC intervention on sprint performance. After a 7-day RIPC intervention, significant changes were noted in the SHAM group's performance metrics postmatch. There was an increase (P < .001) in fatigue index from 22% (8%) to 30% (9%) during repeated-sprint ability test and a decrease in serve speed from 120.2 (17.5) to 106.7 (13.0)km/h (P = .002) and knee peek torque from 196.0 (49.0) to 162.7 (39)N (extension, 60°/s, P < .001) in the SHAM group 24hours postmatch, relative to the IPC group. Moreover, compared with the SHAM group, the IPC group showed a lower rate of perceived exertion during the match (P < .001) and a decrease in visual analog scale score (P = .026) 24hours postmatch, suggesting enhanced recovery and reduced perception of pain relative to the SHAM group. IPC could serve as a strategy to generate an ergogenic effect and recovery during training and competition.