Support Tool Research Articles

Cadherin-17 as a target for the immunoPET of adenocarcinoma.

Cadherin-17 (CDH17) is a calcium-dependent cell adhesion protein that is overexpressed in several adenocarcinomas, including gastric, colorectal, and pancreatic adenocarcinoma. High levels of CDH17 have been linked to metastatic disease and poor prognoses in patients with these malignancies, fueling interest in the protein as a target for diagnostics and therapeutics. Herein, we report the synthesis, in vitro validation, and in vivo evaluation of a CDH17-targeted 89Zr-labeled immunoPET probe. The CDH17-targeting mAb D2101 was modified with an isothiocyanate-bearing derivative of desferrioxamine (DFO) to produce a chelator-bearing immunoconjugate - DFO-D2101 - and flow cytometry and surface plasmon resonance (SPR) were used to interrogate its antigen-binding properties. The immunoconjugate was then radiolabeled with zirconium-89 (t1/2 ~ 3.3 days), and the serum stability and immunoreactive fraction of [89Zr]Zr-DFO-D2101 were determined. Finally, [89Zr]Zr-DFO-D2101's performance was evaluated in a trio of murine models of pancreatic ductal adenocarcinoma (PDAC): subcutaneous, orthotopic, and patient-derived xenografts (PDX). PET images were acquired over the course of 5 days, and terminal biodistribution data were collected after the final imaging time point. DFO-D2101 was produced with a degree of labeling of ~ 1.1 DFO/mAb. Flow cytometry with CDH17-expressing AsPC-1 cells demonstrated that the immunoconjugate binds to its target in a manner similar to its parent mAb, while SPR with recombinant CDH17 revealed that D2101 and DFO-D2101 exhibit nearly identical KD values: 8.2 × 10-9 and 6.7 × 10-9 M, respectively. [89Zr]Zr-DFO-D2101 was produced with a specific activity of 185 MBq/mg (5.0 mCi/mg), remained >80% stable in human serum over the course of 5 days, and boasted an immunoreactive fraction of >0.85. In all three murine models of PDAC, the radioimmunoconjugate yielded high contrast images, with high activity concentrations in tumor tissue and low uptake in non-target organs. Tumoral activity concentrations reached as high as >60 %ID/g in two of the cohorts bearing PDXs. Taken together, these data underscore that [89Zr]Zr-DFO-D2101 is a highly promising probe for the non-invasive visualization of CDH17 expression in PDAC. We contend that this radioimmunoconjugate could have a significant impact on the clinical management of patients with both PDAC and gastrointestinal adenocarcinoma, most likely as a theranostic imaging tool in support of CDH17-targeted therapies.

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

A single-arm pilot of MyInspiration: a novel digital resource to support spiritual needs of patients undergoing cancer-directed surgery.

This study aimed to assess the feasibility, acceptability, and satisfaction associated with the MyInspiration intervention, a digital spiritual support tool for patients undergoing cancer surgery. Additionally, we evaluated changes in spiritual well-being and the ability to find meaning in their experience with cancer before and after the intervention. This was a prospective, single-arm pilot study. Feasibility and acceptability were assessed by ratio of participants who completed all assessments among individuals who hadsigned consent forms. Satisfaction was assessed with 5 Likert-style questions around user experience. Patient spiritual well-being and finding meaning in their experience with cancer were measured at baseline and post-intervention. Forty patients were enrolled, the majority of whom were female (80.0%) and diagnosed with breast cancer (52.5%), with an average age of 54.4years (SD = 13.7, range 29.0-82.0). Regarding feasibility and acceptability, 76.9% of patients who consented to participate completed the full study protocol. In assessing satisfaction, 59% of patients were satisfied with the overall experience of MyInspiration. There was no difference in spiritual well-being pre-/post-intervention. There was a difference in pre (M = 1.95, SD = .95) and post (M = 2.23, SD = .86) scores relative to "finding meaning in the cancer experience" with a mean difference of 0.28 (p = 0.008). MyInspiration was feasible and acceptable to patients, and the majority were satisfied with the tool. The intervention was associated with changes in patients' ability to find meaning within their cancer experience. Arandomized control trial is needed to evaluate the efficacy of the tool in a broader population of patients with cancer.

Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content.

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

Towards reducing diagnostic delay in endometriosis in primary care: a qualitative study.

Due to a heterogeneity of symptoms, a lack of an adequate diagnostic test and a lack of awareness, diagnostic delay in endometriosis in primary care on average amounts to 35 months. To determine which interventions are most feasible to reduce time to diagnosis in primary care, focusing on GPs' preferences, the intervention's content, design and implementation. We conducted a qualitative study by performing focus groups with GPs and GP trainees between July and October 2021. Data collection was continued until saturation was obtained. Focus groups were transcribed and openly encoded. Themes were formulated by three independent researchers. Divided over five focus groups 22 GPs and 13 GP trainees participated. Three themes were formulated: increasing awareness, combined intervention and reaching unaware GPs.Suggestions for a combined intervention strategy were adaptation of guidelines, a diagnostic support tool and compulsory education. To reach unaware GPs, participants felt that education should be offered in regional networks and education for GP trainees should be mandatory. A guideline on menstrual symptoms should be considered, and the term endometriosis should be added to the differential diagnosis paragraphs of existing guidelines. A diagnostic support tool should be linked to a guideline and consist of a flowchart with steps starting with the first presentation of symptoms leading to the diagnosis of endometriosis. According to GPs, a combined intervention strategy consisting of an adapted guideline, a diagnostic support tool and education might be successful interventions in reduction of diagnostic delay in endometriosis.

Application of clinical decision support tools for predicting outcomes with vedolizumab therapy in patients with inflammatory bowel disease: A KASID multicentre study.

We aimed to validate clinical decision support tools (CDSTs) to predict real-life effectiveness of vedolizumab (VDZ) in patients with inflammatory bowel disease. We retrospectively enrolled patients with Crohn's disease (CD) or ulcerative colitis (UC) treated with VDZ at 10 tertiary referral centres in Korea between January 2017 and November 2021. We assessed clinical remission (CREM) and response (CRES), corticosteroid-free clinical remission (CSF-CREM) and response (CSF-CRES), biochemical response based on C-reactive protein (BioRES[CRP]) and faecal calprotectin (BioRES[FC]), endoscopic healing (EH), and the need to optimise or switch drugs based on CDST-defined response groups. Additionally, the area under the receiver operating characteristics curve (AUC) for the CDSTs was calculated. We included 143 patients with CD and 219 with UC. We observed incremental trends on CSF-CRES at week 14 (W14) (ptrend = 0.004) and decreasing trends for the need to optimise or switch drugs (ptrend = 0.016) in CD from the low to high probability groups. Except for CSF-CREM at W54, we noticed incremental trends for all clinical responses at W14, W26 and W54 (ptrend <0.001) in UC. W26 and W54 BioRES[CRP] and W14 EH also showed increasing trends (ptrend <0.05) in UC. With increasing probabilities of response, drug optimisation or switching was less frequently required in UC (ptrend = 0.013). With 26 points cut-off, CDSTs effectively identified W14 CSF-CRES, W26 BioRES[CRP], BioRES[FC] and W54 BioRES[CRP] in UC, all with AUCs >0.600, whereas CDSTs showed poor accuracy in CD. CDSTs for VDZ had acceptable accuracy in predicting effectiveness outcomes including clinical and biochemical outcomes in UC. However, their utility in CD was limited.

Body mapping as a site to negotiate eating struggles and food insecurity for street-involved and homeless youth

Inspired by critical trauma and embodiment theories, this study aims to illustrate how an arts-based approach such as body mapping assists in exploring the lived experiences of youth, potentially serving as a trauma-informed approach. This qualitative study collaborated with street-involved and homeless youth (SIHY) who have eating struggles while living in situations of food insecurity and other forms of oppression. Eleven participants partook in three individual face-to-face interview sessions and one arts-based body map activity, respectively, at a local SIHY resource centre in a metropolitan city in Canada. Guided by Interpretative Phenomenological Approach (IPA), our findings illustrated how body mapping (1) enabled a deepened understanding of SIHY’s eating struggles as both a form of suffering and an embodied means of coping with food insecurity and other systemic and relational trauma(s); (2) provided a transformative experience leading to greater self-compassion and healing; and (3) served as a trauma-informed method that fostered choice and validation. We attest that, as a creative and supportive clinical and research tool, body mapping taps into the unspoken, expressive, embodied, and somatic aspects of eating struggles, food insecurity, poverty, and other forms of oppression deepening knowledge and informing social work research and practice.

Predicting fat cover in beef cattle to make on-farm management decisions: a review of assessing fat and of modeling fat deposition

Abstract Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: (1) conduct a brief review of the biological basis of adipose tissue accretion, (2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, (3) review fat deposition models: Davis growth model (DGM), French National Institute for Agricultural Research growth model (IGM), Cornell Value Discovery System (CVDS), and BeefSpecs drafting tool (BeefSpecsDT) and (4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 days in a commercial feedlot. Evaluating the observed and predicted fat mass (FM) is the focus of this review. The FM mean bias (MB) for Salers heifers were 7.5 and 1.3kg and the root mean square error of prediction (RMSEP) were 31.2 and 27.8kg and for Angus-Hereford steers the MB were -4.0 and -10.5kg and the RMSEP were 9.14 and 21.5kg for DGM and IGM, respectively. The FM MB for Angus steers were -5.61 and -2.93kg and the RMSEP were 12.3 and 13.4kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21, 12, and 68% and 5, 4, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20kg level of tolerance 91 and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of: (1) striving to minimize inputs, and (2) choosing on-farm inputs that are readily available.

Application of ChatGPT as a support tool in the diagnosis and management of acute bacterial tonsillitis

Application of ChatGPT as a support tool in the diagnosis and management of acute bacterial tonsillitis

Perspectives on systematic capacity building in pharmaceutical regulation for regulators of medical products

Having a robust, integrated regulatory system is important for ensuring the availability of safe and efficacious medical products of good quality and for protecting public health. However, less than 30% of countries globally have reached the required regulatory maturity level three, with low- and middle-income countries facing challenges in attracting and retaining qualified staff. World Health Organization (WHO) advocates for systematic workforce development, including competency-based education, to address these gaps. We provide perspectives on a systematic approach to capacity building of medicine regulators based on the experience and lessons learnt in developing and piloting the WHO global competency framework for medicine regulators through three scenarios. A systematic approach to capacity building, such as the human performance technology model, can be used to implement the WHO competency framework as part of organizational performance improvement while ensuring that initiatives are well-defined, targeted, and aligned with organizational goals. The competency framework can be used in different contexts, such as improving organization performance for individual regulatory authorities, strengthening regional collaborations, harmonization and reliance on medical products assessment and joint good manufacturing practices inspections of pharmaceutical manufacturers, and developing learning programs for medicine regulators. A competency-based learning approach for regulatory professionals ensures the transfer of learning to the workplace by integrating real-world practices in learning activities and assessments. Further work is required to develop and validate the assessment instruments, apply the competency framework in other contexts, expanding the learning programmes while continuously providing feedback for further refinement of the competency framework and implementation support tools.

Building a Hierarchical Enablers Framework for Service Business Model Innovation for Sustainable Performance: Evidence from Ghana’s Electricity Sector

Selecting an appropriate business model innovation for sustainable performance is a complex decision that requires a decision support tool. However, despite the importance of business model innovation (BMI) for sustainable performance, there has been limited investigation into how a hierarchical enabler framework grounded in service-dominant logic contributes to the sustainability of service firms. This study examines the critical enablers of service business model innovation (SBMI) for sustainable performance within the utility sector, particularly the electricity supply sector in Ghana. Using the best–worst method (BWM), this study identifies and prioritizes three main enablers and eleven sub-enablers, addressing a notable gap in understanding their impact on sustainable performance. The findings reveal that service value creation innovation is the most critical primary enabler, with human capital, technological platforms, and value-based pricing constituting the top three sub-enablers for sustainability performance. This study contributes to the service-dominant logic and BMI discourse by providing a novel hierarchical framework that aids managerial decision making in service-oriented firms, particularly in developing economies. The results underscore the need for utility companies to prioritize investments in key areas, such as human capital, technological advancements, and customer-centric approaches, to drive sustainable business practices and improve overall performance.

Improving a nitrogen mineralization model for predicting unfertilized corn yield

AbstractCrop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (ε). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha−1; r2 = 0.69). In the new model, clay has a stronger influence than sand on ε, corresponding to lower predicted mineralization rates on fine‐textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha−1; r2 = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions.

Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning

BackgroundPersonalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program.MethodsWe trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model’s predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics.ResultsThe prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases.ConclusionBased on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling.

Analysis of Learners' Emotions in E-Learning Environments Based on Cognitive Sciences

The present study aimed to examine students’ emotions in e-learning classes through facial expressions and investigate the influence of different instructional methods on students’ emotional responses. In this study, we examined the facial expressions of 17 undergraduate students using three different methods of presenting educational content (PowerPoint, video, and Kahoot) in online classes and analyzed the data with face reader software. The findings demonstrated that students experienced various positive and negative emotions with different methods of content delivery. Furthermore, comparing the three methods revealed that the Kahoot method elicited the highest average of positive emotions among students compared to the other two methods. This difference can be attributed to the visual attractiveness and interactive nature of the Kahoot environment. Additionally, this study highlights that simply incorporating multimedia materials, such as PowerPoint presentations and videos, is not sufficient to enhance effectiveness and cultivate positive emotions in e-learning. While multimedia materials serve as supportive tools and enhance visualization, interaction at various levels (content, teacher, peers, etc.) is necessary. Nevertheless, the significance of this research lies in the innovative application of a tool for analyzing emotions in online learning classrooms, thereby enhancing the measurement of genuine and objective emotional responses in e-learning environments.

Nutritional and Lifestyle Features in a Mediterranean Cohort: An Epidemiological Instrument for Categorizing Metabotypes Based on a Computational Algorithm

Background and Objectives: Modern classification and categorization of individuals’ health requires personalized variables such as nutrition, physical activity, lifestyle, and medical data through advanced analysis and clustering methods involving machine learning tools. The objective of this project was to categorize Mediterranean dwellers’ health factors and design metabotypes to provide personalized well-being in order to develop professional implementation tools in addition to characterizing nutritional and lifestyle features in such populations. Materials and Methods: A two-phase observational study was conducted by the Pharmacists Council to identify Spanish nutritional and lifestyle characteristics. Adults over 18 years of age completed questionnaires on general lifestyle habits, dietary patterns (FFQ, MEDAS-17 p), physical activity (IPAQ), quality of life (SF-12), and validated well-being indices (LS7, MEDLIFE, HHS, MHL). Subsequently, exploratory factor, clustering, and random forest analysis methods were conducted to objectively define the metabotypes considering population determinants. Results: A total of 46.4% of the sample (n = 5496) had moderate-to-high adherence to the Mediterranean diet (&gt;8 points), while 71% of the participants declared that they had moderate physical activity. Almost half of the volunteers had a good self-perception of health (49.9%). Regarding lifestyle index, population LS7 showed a fair cardiovascular health status (7.9 ± 1.7), as well as moderate quality of life by MEDLIFE (9.3 ± 2.6) and MHL scores (2.4 ± 0.8). In addition, five metabotype models were developed based on 26 variables: Westernized Millennial (28.6%), healthy (25.1%), active Mediterranean (16.5%), dysmetabolic/pre-morbid (11.5%), and metabolically vulnerable/pro-morbid (18.3%). Conclusions: The support of tools related to precision nutrition and lifestyle integrates well-being characteristics and contributes to reducing the impact of unhealthy lifestyle habits with practical implications for primary care. Combining lifestyle, metabolic, and quality of life traits will facilitate personalized precision interventions and the implementation of targeted public health policies.

Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study

Abstract Aims An algorithmic strategy for anatomical vs. functional testing in suspected coronary artery disease (CAD) (Anatomical vs. Stress teSting decIsion Support Tool; ASSIST) is associated with better outcomes than random selection. However, in the real world, this decision is rarely random. We explored the agreement between a provider-driven vs. simulated algorithmic approach to cardiac testing and its association with outcomes across multinational cohorts. Methods and results In two cohorts of functional vs. anatomical testing in a US hospital health system [Yale; 2013–2023; n = 130 196 (97.0%) vs. n = 4020 (3.0%), respectively], and the UK Biobank [n = 3320 (85.1%) vs. n = 581 (14.9%), respectively], we examined outcomes stratified by agreement between the real-world and ASSIST-recommended strategies. Younger age, female sex, Black race, and diabetes history were independently associated with lower odds of ASSIST-aligned testing. Over a median of 4.9 (interquartile range [IQR]: 2.4–7.1) and 5.4 (IQR: 2.6–8.8) years, referral to the ASSIST-recommended strategy was associated with a lower risk of acute myocardial infarction or death (hazard ratioadjusted: 0.81, 95% confidence interval [CI] 0.77–0.85, P &amp;lt; 0.001 and 0.74 [95% CI 0.60–0.90], P = 0.003, respectively), an effect that remained significant across years, test types, and risk profiles. In post hoc analyses of anatomical-first testing in the Prospective Multicentre Imaging Study for Evaluation of Chest Pain (PROMISE) trial, alignment with ASSIST was independently associated with a 17% and 30% higher risk of detecting CAD in any vessel or the left main artery/proximal left anterior descending coronary artery, respectively. Conclusion In cohorts where historical practices largely favour functional testing, alignment with an algorithmic approach to cardiac testing defined by ASSIST was associated with a lower risk of adverse outcomes. This highlights the potential utility of a data-driven approach in the diagnostic management of CAD.

Data Cleaning: Challenges and Existing Solutions

We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning.

System-Wide Thromboprophylaxis Interventions for Hospitalized Patients at Risk of Venous Thromboembolism: Focus on Cross-Platform Clinical Decision Support.

Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. We conducted a narrative review summarizing system-wide thromboprophylaxis interventions in hospitalized patients. Multiple interventions for thromboprophylaxis have been tested, including multifaceted approaches such as national VTE prevention programs with audits, pre-printed order entry, passive alerts (either human or electronic), and more recently, the use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system and order entry interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE unless mandated through a national VTE prevention program, though the latter approach is potentially costly and effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. Recently, a universal cloud-based and EHR-agnostic CDS VTE tool incorporating a validated VTE risk score revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. Active CDS tools hold promise in improving appropriate thromboprophylaxis, especially with further refinement and widespread implementation within various EHRs and clinical workflows.

Integration of chatbots into foreign language learning

Innovative technologies are a part and parcel of modern education. Being integrated in the education process, neural network technologies are of particular interest now. Chatbots are used for different purposes: to gain and consolidate knowledge, to test knowledge, to study foreign languages, to organize the education process, etc. The use of generative AI contributes to education efficiency, improves skills and students’ motivation. One of the most popular and promising generative neural networks is ChatGPT. The use of this chatbot got mixed reviews, and this article examines the possibilities of using chatbots in education. We attempt to use ChatGPT as a support tool for compiling a training manual on developing listening skills for EFL (English as a foreign language) students. We analyze the advantages and disadvantages of using ChatGPT for compiling teaching materials and outline recommendations on how to use this tool efficiently. We found that, in general, the use of ChatGPT optimizes the process of preparing training materials for intermediate-level students. However, it is important to take into account a number of features such as the language register, the genre and text type, semantic breakdown of the text, the risk of neural network making an error, etc. The article concludes that the chatbot, if used correctly, can become an effective virtual assistant for an EFL teacher.