Support Tool Research Articles

The Effects of Rubrics and Exemplars as Self-regulated Learning Tools on Students’ Development of Evaluative Judgement of Intercultural Competence: An Exploratory Study

This study aimed to explore the effects of a pedagogical intervention involving the combined application of rubrics and exemplars, as Self-Regulated Learning (SRL) support tools, on student’s development of the Evaluative Judgement (EJ) of Intercultural Competence (IC). A group of students from the General Required Courses department of a Saudi college participated in a quasi-experimental study involving the administration of an English writing module as part of an intensive English course. Coursework was designed based on Byram’s multidimensional IC model, guided by Liddicoat’s pathway model for IC development, framed within Panadero & Broadbent’s EJ framework, and mapped on Zimmerman’s Self-Reflection Phase (SRP) of the self-regulatory process. A mixed-method design was adopted to gain insights on the learning processes the students went through as well as their perceptions of and attitudes towards the learning experience. The results indicated a variety of SRP strategies at work and provided evidence for the positive effects of rubrics and exemplars on the enhancement of students’ EJ of IC. The results revealed several spheres where SRL, EJ, and IC relate and align to support students’ development and practice of sustainable skills that allow them to take control of their own learning and adapt in ever-changing environments.

Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNETICS™ validation study

BackgroundAquaculture, traditionally a form of biotechnology, has evolved to integrate innovative biotechnological applications, such as advanced feed formulations, aimed at improving the growth performance and health of farmed fish species. In the present study, the effects of feeding rainbow trout with novel feed formulations were investigated. Fish growth, gut and liver morphology, the concentration of fatty acids in the fillet, and volatile fatty acids in the gut were assessed. The study also validated scenarios from in vivo experiments using a nutrient-based model called FEEDNETICS™. This globally used model serves as a tool for data interpretation and decision support in the context of precision fish farming.MethodsAlternative protein and oil sources, including poultry by-product meal (PBM) and natural algae oil, were explored as sustainable replacements for fishmeal (FM) and fish oil (FO). A 90-day feeding trial was conducted using rainbow trout, comparing two isoproteic, isolipidic and isoenergetic diets. The control diet contained 15% FM, 5% PBM, and 8% FO, while the test diet replaced FM with 15% PBM and 5% feather meal hydrolysate (FMH), and fully substituted FO with VeraMaris® natural algae oil and rapeseed oil.ResultsPBM successfully replaced FM protein without negatively affecting feed intake, growth performance or feed utilization in trout. The combination of PBM and natural algae oil was well tolerated by the trout and showed no negative effects on gut health. A detailed analysis of fatty acids in the fillet revealed that PUFAs of the n3 and n6 series were significantly higher in the PBM group than in the FM group. Values of fatty acid-related health indexes, including atherogenicity index, and thrombogenicity index, confirmed the high nutritional value of trout filet, thus representing a healthy product for human. In addition, the predictions using the FEEDNETICS™ indicated that the tested novel alternative formulations are economically viable. The validation of the model for fish growth resulted in a Mean Absolute Percentage Error (MAPE) of 8%.ConclusionsThe FEEDNETICS™ application enhances our ability to optimize feeding strategies and improve production efficiency in the aquaculture industry. VeraMaris® algae oil and PBM could serve as viable and sustainable raw materials for fish feed, promoting environmentally friendly aquaculture practices.

Students’ perceptions of an artificially intelligent chatbot as a support tool to develop their research skills

PurposeThis research aims to evaluate students’ perception of using a chatbot to develop their understanding of the various parts of a research article, and their ability to apply what they have learned to write a new research plan.Design/methodology/approachThis research is qualitative in approach. The sample contained 10 students on a research skills course as part of the master of educational technologies program. The research instruments consisted of (1) semi-structured interviews to gather the students’ perceptions of the effectiveness of using a chatbot, and (2) observation notes to triangulate the interview results, relating to the students’ application of what they had learned from the teacher and ChatGPT, in new contexts.FindingsThis study found that the use of ChatGPT contributed to developing students’ knowledge of writing a research plan. The students were satisfied with their personalized learning experience, and believed that ChatGPT had improved their autonomy, competence and relatedness. These three variables had played a role in developing their application skill. However, there were concerns about using ChatGPT in learning, like limiting the students’ creativity, human interaction and critical thinking skills. There were also concerns over accuracy, ethics and plagiarism.Originality/valueThe originality of this study lies in its examination of how ChatGPT can help satisfy students' need for autonomy (having more knowledge to communicate with the app and control their choices), competence (feeling more capable of using artificial intelligence [AI] and possessing the necessary knowledge and skills to succeed) and relatedness (being less disappointed when failing to obtain satisfactory responses from the AI application), while improving their skills in application. According to self-determination theory (SDT), these factors affect students’ skills in applying what they have learned to new tasks. Therefore, this study extends the examination of SDT and its effect to include application skill, using qualitative methods to study the relationships between variables in depth.

Pediatric High Blood Pressure Recognition Associated With Electronic Decision Support: A Cohort Analysis.

Pediatric high blood pressure (BP) predicts future cardiovascular disease events. High BP is improperly measured, underrecognized, and undermanaged especially in disadvantaged populations. In a large, diverse, academic pediatric practice, we detail the associations of a comprehensive initiative with high BP provider recognition. A comprehensive BP initiative was promulgated including (1) retraining providers and staff on BP management; (2) deploying equipment at 55+ sites; and (3) electronic decision support tool alerting staff and clinicians and suggesting management. During the 14-month preintervention and 14-month postintervention periods, data on BP and patient characteristics were collected. The outcome was incident BP recognition defined as any of the following: BP-specific International Classification of Diseases, Tenth Revision, diagnosis; problem list entry; specialty referral; diagnostic testing; repeat visit; or antihypertensives. Dichotomized as underpre versus at or over 13 years of age, analyses utilized interrupted time series and multivariable-adjusted logistic regression. From preintervention (children, n=105 674; adolescents, n=54 365) to postintervention (children, n=87 917; adolescents, n=56 470), the proportion measured with high BP declined in children and adolescents (30% versus 14% and 30% versus 15%, respectively, each P<0.001). Post-intervention, high BP provider recognition was 58% higher in children (4.6%-7.3%) and 43% higher in adolescents (7.9% versus 11.3%; P<0.001 for both). The improvement was not different in disadvantaged race, ethnicity, or zip codes. A comprehensive BP initiative was associated with more appropriate high BP measurement and recognition including in disadvantaged populations. Future work may address the low overall and nonsustained recognition and consideration of electronic decision support for pediatric BP management and mitigating disparities.

Effects of an Infant/Toddler Intervention at 1-Year Follow-Up: Sustained Impacts to Preschool Entry

We Learn Together is a 20-week, low-cost infant/toddler school-readiness intervention developed to provide instructional content and supportive tools for teachers to be more explicit and intentional in interactions with children to support early development. Short-term effects were established in a previously published real-world effectiveness randomized controlled trial. In the present quasi-experimental study, we examined the extent to which the effects remained approximately 1 year after the intervention. Because the original control group eventually also got the intervention, we compared mandatory municipal language assessment outcomes of 2,327 children in the treatment group to matched nontreatment children. The intervention effect was maintained at similar magnitudes at the 1-year follow-up for oral language skills (effect sizes = 0.09–0.14), and spillover effects were found for rhyming skills (effect size = 0.12).

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure.

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

Clean Energy Challenges and Innovation Opportunities in Kazakhstan

Abstract Kazakhstan has pledged to transition to a low-carbon economy by implementing national policies and strategies that promote clean energy innovation. However, Kazakhstan is still falling short of its expected targets for energy transition, and there is a lack of knowledge regarding the country's challenges and opportunities for clean energy development. Towards this end, the current study identifies and assesses the enablers and barriers related to clean energy innovation in Kazakhstan. Using the combination of SWOT analysis, survey data from 41 experts and the DEMATEL decision support tool, we evaluated the key factors affecting Kazakhstan's clean energy innovation and their implications for energy transition. Assessment results show that the immature business environment, underpinned by technological, institutional, and socioeconomic factors, is perceived as a high-impact constraint for clean energy innovation and green finance deployment in Kazakhstan. Skilled labour shortages, high reliance on hydrocarbons and low retail energy prices are significant challenges to Kazakhstan's clean energy innovation. The low-profit margin and high investment risk in clean energy projects are identified as transition barriers in the power and energy-intensive industries. In contrast, Kazakhstan's endowments of resources critical for developing clean energy technologies (rare earth metals, uranium, gas) and the potential of low-carbon investments (e.g. carbon storage) are perceived as prominent enablers of clean energy innovation. Results are consistent across expert subgroups (academia, industry, NGOs, etc). Findings call for policy support to modern and attractive business environments, capacity, and human capital development. The findings can provide helpful insights for countries in Central Asia and beyond with similar socioeconomic structures that aim for a timely energy transition.

VASARI-AUTO: PERFORMANT, EQUITABLE, EffiCIENT, ECONOMIC, AND SURVIVAL PREDICTIVE FEATURISATION OF GLIOMA MRI

Abstract AIMS The VASARI (Visually AcceSAble Rembrandt Images) MRI feature set is a quantitative system designed to standardize glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and therefore seldom used in clinical practice. This is however a problem that performant machine-learning software could plausibly automate. METHOD In 100 patients, two consultant neuroradiologists independently quantified VASARI features. In parallel, we developed VASARI-auto, an automated VASARI-labelling software applied to both open-source hand-segmented lesion masks and our openly available tumour segmentation model. We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of patient equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient overall survival. Neuroradiologists were blinded to VASARI-auto software development and evaluations, and software developers were blinded to neuroradiologist labelling. RESULTS Segmentation and VASARI-auto were equally performant regardless of age or sex (mean segmentation Dice coefficient 0.947). A modest inter-rater variability between neuroradiologists (mean Cohen’s Kappa 0.49) was comparable between neuroradiologists and VASARI-auto (mean Cohen’s Kappa 0.41), with higher agreement across VASARI-auto methods (mean Cohen’s Kappa 0.94). The time taken for neuroradiologists to derive VASARI features was substantially higher than VASARI-auto (mean time per case 317 vs. 3 seconds, p&amp;lt;0.0001). A hospital workforce analysis of our centre forecast that three years of VASARI glioma featurisation would demand 771 consultant neuroradiologist workforce hours (£40,789) or 8.6 hours of computing time (£3.76 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features (R2 0.25), opposed to those derived by consultant neuroradiologists (R2 0.21). CONCLUSION VASARI-auto is a highly efficient automated labelling system for VASARI featurisation with equitable performance regardless of patient age or sex, a favourable economic profile if used as a decision support tool, and with non-inferior fidelity in downstream patient survival prediction. Future work should integrate such tools to enhance patient care.

Ordering and Utilization of Peripheral Blood Flow Cytometry in an Academic Medical Center

Abstract Introduction/Objective The American Society for Clinical Pathology and the American Board of Internal Medicine’s Choosing Wisely campaign have developed best practice utilization guidelines for laboratory testing. Wisconsin Diagnostic Laboratories and Medical College of Wisconsin leadership noticed an increase in inappropriate peripheral blood flow cytometry testing in the last two years. An intervention was developed to help reduce inappropriate testing requests. Methods/Case Report The laboratory implemented a clinical decision support tool in the electronic medical record system that looked at patient diagnosis, history of stem cell transplant, and recommendations from pathology to help improve utilization of peripheral blood flow cytometry testing. Based on patient history, flow cytometry testing would move forward, or an alternative order of a peripheral blood pathology smear review with reflex to flow cytometry was offered. Pathologists would reflex flow cytometry if abnormalities were present by light microscopy or predetermined reflexing criteria were met. Results (if a Case Study enter NA) 314 cases were included for analysis. The intervention reduced volumes of peripheral blood flow cytometry in patients without a history of hematologic malignancy by 49.1% (p=&amp;lt;0.001). Appropriate utilization increased from 47.0% in the pre-intervention period to 80.4% in the post intervention period (p=&amp;lt;0.001). Significant diagnosis rate increased from 42.5% to 50.0% (p=.261). This resulted in a cost savings of $12,232 for the health system during the intervention period. Conclusion Clinical decision support based utilization efforts provide strong and consistent interventions to reduce the unnecessary and inappropriate ordering of peripheral blood flow cytometry.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

AI-supported decision-making in obstetrics - a feasibility study on the medical accuracy and reliability of ChatGPT

The aim of this study is to investigate the feasibility of artificial intelligence in the interpretation and application of medical guidelines to support clinical decision-making in obstetrics. ChatGPT was provided with guidelines on specific obstetric issues. Using several clinical scenarios as examples, the AI was then evaluated for its ability to make accurate diagnoses and appropriate clinical decisions. The results varied, with ChatGPT providing predominantly correct answers in some fictional scenarios but performing inadequately in others. Despite ChatGPT's ability to grasp complex medical information, the study revealed limitations in the precision and reliability of its interpretations and recommendations. These discrepancies highlight the need for careful review by healthcare professionals and underscore the importance of clear, unambiguous guideline recommendations. Furthermore, continuous technical development is required to harness artificial intelligence as a supportive tool in clinical practice. Overall, while the use of AI in medicine shows promise, its current suitability primarily lies in controlled scientific settings due to potential error susceptibility and interpretation weaknesses, aiming to safeguard the safety and accuracy of patient care.

A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model’s application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs.

Unveiling the Inter-Related Preferences of Crowdworkers: Implications for Personalized and Flexible Platform Design

Crowdsourcing platforms have traditionally been designed with a focus on workstation interfaces, restricting the flexibility that crowdworkers need. Recognizing this limitation and the need for more adaptable platforms, prior research has highlighted the diverse work processes of crowdworkers, influenced by factors such as device type and work stage. However, these variables have largely been studied in isolation. Our study is the first to explore the interconnected variabilities among these factors within the crowdwork community. Through a survey involving 150 Amazon Mechanical Turk crowdworkers, we uncovered three distinct groups characterized by their interrelated variabilities in key work aspects. The largest group exhibits a reliance on traditional devices, showing limited interest in integrating smartphones and tablets into their work routines. The second-largest group also primarily uses traditional devices but expresses a desire for supportive tools and scripts that enhance productivity across all devices, particularly smartphones and tablets. The smallest group actively uses and strongly prefers non-workstation devices, especially smartphones and tablets, for their crowdworking activities. We translate our findings into design insights for platform developers, discussing the implications for creating more personalized, flexible, and efficient crowdsourcing environments. Additionally, we highlight the unique work practices of these crowdworker clusters, offering a contrast to those of more traditional and established worker groups.

Assessing health technology implementation during academic research and early-stage development: support tools for awareness and guidance: a review.

For successful health technology innovation and implementation it is key to, in an early phase, understand the problem and whether a proposed innovation is the best way to solve the problem. This review performed an initial exploration of published tools that support innovators in academic research and early stage development with awareness and guidance along the end-to-end process of development, evaluation and implementation of health technology innovations. Tools were identified from scientific literature as well as in grey literature by non-systematic searches in public research databases and search engines, and based on expert referral. A total number of 14 tools were included. Tools were classified as either readiness level tool (n = 6), questionnaire/checklist tool (n = 5) or guidance tool (n = 3). A qualitative analysis of the tools identified 5 key domains, 5 innovation phases and 3 implementation principles. All tools were mapped for (partially) addressing the identified domains, phases, and principles. The present review provides awareness of available tools and of important aspects of health technology innovation and implementation (vs. non-technological or non-health related technological innovations). Considerations for tool selection include for example the purpose of use (awareness or guidance) and the type of health technology innovation. Considerations for novel tool development include the specific challenges in academic and early stage development settings, the translation of implementation to early innovation phases, and the importance of multi-disciplinary strategic decision-making. A remaining attention point for future studies is the validation and effectiveness of (self-assessment) tools, especially in the context of support preferences and available support alternatives.

Application of the Medical Research Council guidance for complex interventions in the development of VIeSA, an intervention to support healthy ageing among community-dwelling older adults

BackgroundThe ageing population, coupled with the desire to age-in-place, highlight the need for programs that target health promotion as a means of maintaining functional ability, autonomy, and independence among community-dwelling older adults. This paper describes the development of the VIeSA intervention, which aimed to model a healthy ageing trajectory, including the identification of the necessary tools and methods, that would allow people older people, in partnership with health and social care professionals, to define personal health-related goals and the actions to achieve them. A key element of the intervention development was the creation of a support tool intended to assist this process.MethodsThe UK Medical Research Council (MRC) guidance on developing and evaluating complex interventions was applied in the development of the intervention and of the support tool. A participatory approach was selected, with stakeholders engaged on different occasions to allow the refinement of the intervention and of the support tool. Following the steps and suggested actions in the MRC development phase, the development process was conducted by identifying (1) the evidence base and (2) the theoretical framework and (3) by modelling the process and outcomes of the intervention.ResultsFollowing a literature review on effective interventions for functional ability, draft 1 of the support tool was designed. Focus groups with stakeholders provided feedback on this draft allowing for its refinement in terms of content, language use, and structure (draft 2). A review of the approaches for health promotion delivery led to further additions to the tool (draft 3) and informed the content of the training of health and social care professionals. After their training, professionals provided feedback on the acceptability, appropriateness, and feasibility of different elements of the intervention. Results suggested that no further major refinement to the intervention or support tool was necessary.ConclusionsThe design and development of the VIeSA intervention using the MRC guidance allowed for a clarity of direction, an optimised content in terms of usefulness and accessibility for all concerned stakeholders, and greater opportunities for its implementation and uptake.

Decision Support Tool to Improve Decision-Making for HIV Pre-Exposure Prophylaxis (PrEP): Development Process and Alpha Testing.

African, Caribbean, and Black (Black) communities in Canada are disproportionately affected by the HIV epidemic. Pre-exposure prophylaxis (PrEP) is a highly effective option for the prevention of HIV. However, the use of PrEP for HIV prevention among eligible Black clients in Canada remains far below the thresholds necessary to achieve the goal of zero new HIV infections. In a recent study in Toronto, PrEP-eligible Black clients were found to have decisional conflict and unmet decisional needs, which affected the quality of their decision-making process regarding the initiation and adherence to PrEP. There is evidence that decision support tools (DSTs) can improve the quality of a decision, the quality of the decision-making process, the implementation or continuation of the chosen option, and the appropriate use of health services. Despite these benefits, there is currently no DST for PrEP-eligible Black clients being asked to consider PrEP for HIV prevention. Our study aimed to develop a DST to improve PrEP decision-making for Black clients and to evaluate the tool's acceptability and usability. We developed and evaluated the PrEP DST for Black patients using the 7-step process outlined in the Ottawa Decision Support Group Guideline for the development and evaluation of DST. To facilitate the implementation of the Ottawa Decision Support Group guideline, we assembled a multidisciplinary team of primary health care providers, researchers, community members with lived experiences, and digital content designers to serve as the steering committee. First, we assessed patients' and primary health care providers' views on decisional support needs, after which we determined the content, design, and distribution plan for the DST. Subsequently, we conducted evidence synthesis, reviews, and appraisal before developing the PrEP DST prototype. The final tool was reviewed by steering committee members for completeness before acceptability and usability testing with potential Black clients and PrEP providers. The web-based DST yielded 27 pages divided into 6 distinct sections. The six sections include (1) an introduction of the DST, (2) clarify your decision, (3) knowledge, (4) a value clarification exercise, (5) support system, and (6) next steps. Both Black clients and PrEP providers reported ease of task performance, general satisfaction, and usefulness of the tool to support decision-making for Black clients. Feedback on usability centered on the need to add a user guide to increase usability. All feedback was incorporated into the final tool. A PrEP DST for Black clients developed using a systematic process and a multidisciplinary steering committee was acceptable and usable by both Black clients and PrEP providers. Further study (eg, randomized controlled trials) may be needed to evaluate the efficacy of the PrEP DST.

Support Tool for Fractions Concepts in Basic Education

Support Tool for Fractions Concepts in Basic Education

Computed tomography referral guidelines adherence in Europe: insights from a seven-country audit.

Ensuring appropriate computed tomography (CT) utilization optimizes patient care while minimizing radiation exposure. Decision support tools show promise for standardizing appropriateness. In the current study, we aimed to assess CT appropriateness rates using the European Society of Radiology (ESR) iGuide criteria across seven European countries. Additional objectives were to identify factors associated with appropriateness variability and examine recommended alternative exams. As part of the European Commission-funded EU-JUST-CT project, 6734 anonymized CT referrals were audited across 125 centers in Belgium, Denmark, Estonia, Finland, Greece, Hungary, and Slovenia. In each country, two blinded radiologists independently scored each exam's appropriateness using the ESR iGuide and noted any recommended alternatives based on presented indications. Arbitration was used in case auditors disagreed. Associations between appropriateness rate and institution type, patient's age and sex, inpatient/outpatient patient status, anatomical area, and referring physician's specialty were statistically examined within each country. The average appropriateness rate was 75%, ranging from 58% in Greece to 86% in Denmark. Higher rates were associated with public hospitals, inpatient settings, and referrals from specialists. Variability in appropriateness existed by country and anatomical area, patient age, and gender. Common alternative exam recommendations included magnetic resonance imaging, X-ray, and ultrasound. This multi-country evaluation found that even when using a standardized imaging guideline, significant variations in CT appropriateness persist, ranging from 58% to 86% across the participating countries. The study provided valuable insights into real-world utilization patterns and identified opportunities to optimize practices and reduce clinical and demographic disparities in CT use. Question Largest multinational study (7 EU countries, 6734 CT referrals) assessed real-world CT appropriateness using ESR iGuide, enabling cross-system comparisons. Findings Significant variability in appropriateness rates across institution type, patient status, age, gender, exam area, and physician specialty, highlighted the opportunities to optimize practices based on local factors. Clinical relevance International collaboration on imaging guidelines and decision support can maximize CT benefits while optimizing radiation exposure; ongoing research is crucial for refining evidence-based guidelines globally.

A framework for evaluating tool support for co-evolution of modeling languages, tools and models

A framework for evaluating tool support for co-evolution of modeling languages, tools and models

Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area

Residual biomass from agricultural production and processing, such as citrus pulp and olive pomace, is an important resource for energy production. In particular, this is the case in regions where transformation industries are concentrated.Current biomass estimates often focus on actual production data, that may not fully capture the biomass potential across all suitable cultivation areas. To bridge this gap, the study predicts the overall potential biomass available for energy production, taking into account the total area suitable for citrus cultivation.The research is focused on the study of citrus species in the province of Syracuse, Sicily, Italy. The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. The results highlighted potential citrus cultivation on 47,706 ha and estimated 184,340 t of biomass, 16,461,520.82 Nm3 of biogas, and 8110 t of digestate. The results of the study identified potential areas for both increasing biomass production and optimising the distribution of digestate, thus demonstrating the utility of these thematic maps as a decision support tool for land management. The simulations and the methodology applied in this study indicated potential economic and environmental benefits to be gained from sustainable by-product management. This approach facilitates the optimisation of decision-making processes for land planning, thereby contributing to the broader objective of improving resource efficiency and sustainability in the agricultural production and processing sectors.