Trained Experts Research Articles

A COMPUTER-AIDED BRAIN GLIOMA IDENTIFICATION USING MRI BASED TEXTURE ANALYSIS

Brain gliomas are deadly tumors that are discovered after a stressful, lengthy, and difficult procedure. Radiology is a broad and varied field that detects brain tumors, but interpreting radiological pictures of the brain needs advanced training, experience, and subject-matter expertise. The endeavor is challenging due to the wide variation in brain tumor tissues among individuals and similar cases within normal tissues. Magnetic resonance imaging (MRI)-based computer-aided biomedical image processing solves the challenges associated with brain tumor localization and identification while simultaneously addressing the shortage of qualified radiologists. This study proposes a computer-aided brain glioma identification (CABGD) model for brain glioma identification using the texture analysis of brain MRIs. The proposed model makes use of machine learning classifiers and brain MRI data. There were 200 MRIs of both normal and glioma brains in the experimental dataset. Firstly, the MRI dataset was pre-processed to crop the MRIs, size equalization, and gray-level conversion. Next, noises were removed by applying filters. Two ROIs of the sizes (10 ×10) were taken on each MRI after the tumor region was segmented. After extracting COM texture features from each ROI, thirty optimal features were obtained through a compound supervised feature selection, blend of Fisher (FSHR), probabilistic model of error (POE), average correlation (AVC), and mutual information (MUI). The optimal feature brain MRI dataset was classified into glioma and normal brain by applying machine learning classifiers named Bayas Net (BN), logistic model tree (LMT), and partial decision tree (PART); the classification results of the NB, LMT, and PART classifiers were 79.75%, 82.75, and 85%, respectively.

비수도권 대학 지역 연합 동아리 기반 UX 디자인 프로그램 개발

Recently, the UX design expert training program is founded around Seoul metropolitan area, where firms growing in the online platform industry and global companies are concentrated. Accordingly, there are certain limitations in the environment in which students majoring in UX design at non-metropolitan universities can grow into experts. This research confirmed the possibility of after-school activities ‘clubs' through a pre-test that assessed the positive effects on major learning from club participation among students at non-metropolitan universities. As mentioned above, this research founded the local united club for studying UX design. The local united club recruited students who wanted to study a project-based major. For club activities, which was the UX design program based on content from pre-research, was constructed following the stages of UX design education confirmed in previous studies. After experiencing the 15- week course twice, cross-verification was conducted through students participating in the club to see whether the UX design education program presented in the previous study was a universal education program or a situation of improving local infrastructure of UX design. As a result of the experiment, it was confirmed that the UX design education program, operated in the form of a regional association club, could improve the UX design education infrastructure. The program of after-school club activities based in non-metropolitan areas, which will help students grow into UX design experts in IT firms, will be an alternative to improving the disappointing environment for students in Daegu and Gyungbuk area.

Blockchain-based crowdsourced deep reinforcement learning as a service

Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.

Факторы успешной коммуникации в межпрофессиональном дискурсе (на примере собеседования-интервью)

The need to identify verbal and non-verbal specifics of the Russian-language employment discourse, and the timeliness of analyzing the communicative behavior of job interviews’ participants (professionals from various areas) have determined the relevance of the present study. In terms of its discourse-forming properties, the professional discourse belongs to the institutional type of discourse. However, unlike other discourse types, the communicative environment in this case is considered interprofessional, since the agent (HR specialist), on one hand, and the client (a specialist from other area), on the other hand, represent different corporate cultures. The author of the article concludes that the effectiveness of the job applicant’s strategic program (a self-presentation strategy) depends on his/her ability to balance between authentic modesty and positive self-presentation. The article presents a newly formulated communicative code based on the analysis of the communicative successes and failures of job applicants. It includes a system of guiding principles, thus allowing job applicants to achieve communicative success during a job interview. The findings of the study have application in the training of HR experts, as well as in other areas related to the effectiveness of business communication. The study’s outcomes are significant for all potential employees with regard to the efficiency of the communicative behavior of interview participants.

ENHANCING LOAN ORIGINATION THROUGH DIGITIZATION AND DATA ANALYTICS IN BANK BRI

Digitalization and data analytics in loan origination offer numerous benefits to banking institutions, including increased processing speed, improved decision-making, cost efficiencies, better customer experiences, and easier regulatory compliance. However, these advancements come with challenges such as cybersecurity risks, high initial costs, privacy concerns, technology adaptation difficulties, and system integration complexities. By implementing appropriate solutions, such as improving cybersecurity, conducting financial analyses, ensuring transparent data use, providing training, and seeking expert assistance, banks can address these limitations and fully leverage the benefits of enhanced loan origination efficiency. The adoption of digitalization and data analytics has transformed traditional loan origination processes into streamlined and efficient systems. Digitalization has enabled banks to automate workflows, reduce processing times, and improve customer experiences. Data analytics has provided banks with insights into customer behavior, credit risk, and fraud detection

Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness

To maximize training programs and improve individual performance, the sports fitness profession is always looking for new and innovative solutions. Fuzzy decision support systems provide a strong basis for improving training regimen efficacy through flexible and adaptive decision-making. In sports fitness, doing things differently might affect the way one can train and even lead to injury. Particularly if exercises call for expert training regimens, determining if fitness requirements are sufficiently satisfied is a difficult task. Athletes participate in training programs focusing on strengthening their bodies and minds to perform better. Each sport has unique needs, and athletes should prepare to meet those demands while still meeting their sport’s general fitness activity requirements. A novel strategy for assisting athletes with fitness-related decision-making is presented in this study as the Fitness Mamdani Decision System. At the outset, the system provides an adaptive decision framework to improve the effectiveness of training programs by applying the principles of intuitionistic fuzzy numbers and fuzzy logic. The study wants to know how to train effectively, and it takes important factors like your mood, degree of preparation, sleep quality, and stress levels into account. Using language-specific terminology and triangle membership functions, the Mamdani fuzzy inference system generates rules based on analyzing these crucial elements. Regarding measures like adaptability index, training load capacity, long-term program efficiency, and participation ratio among sports fitness individuals, the system is guided by fuzzy rules that infer decisions.

Assessment of occupational exposure to lead among workers engaged in a city bus garage in Addis Ababa, Ethiopia: a comparative cross-sectional study

BackgroundLead is one of the most nonessential toxic heavy metal agents found in automotive garages. The occupational exposure of garage workers to lead commonly poses acute and chronic health risks that can be prevented. In Ethiopia, there have been limited studies on lead exposure among garage workers, who overemphasize exposure to lead. This study aimed to assess occupational blood lead levels and associated factors in garage workers using a cross-sectional comparative design.MethodsA comparative cross-sectional study design was used to compare the occupational blood lead levels of 36 randomly selected garage workers and 34 office workers who were matched by age and sex. Blood specimens were collected by trained medical laboratory experts. The collected blood samples were tested in a certified laboratory using a microwave plasma atomic emission spectrometry (MP-AES) device at a wavelength of 405.78 nm. Excel and SPSS Version 26 were used for data management and analysis, respectively.ResultsThe mean (SD) age of the exposed group was 39.0 (7.5) years, whereas the mean age of the unexposed group was 38.0 (6.1) years. The occupational mean (SD) blood-lead-level in the exposed groups was 29.7 (12.2) µg/dl, compared to 14.8 (9.9) µg/dl among the unexposed groups. The mean blood-lead level among the exposed workers was significantly different from that among the unexposed workers (P < 0.01). Of all the study participants, only 22.2% of the exposed groups had blood lead levels higher than the World Health Organization’s recommended limit of 40 µg/dl. The main significant predictors of occupational blood-lead-level exposure among workers were extra working hours, service years, and having a previous (prior) employment history in a garage. The occupations of the two groups did not significantly differ in terms of blood-lead levels (p > 0.05).ConclusionsThe BLL of the Garage workers was significantly greater than that of the Non-Garage workers. Hence, it is advised that garage management should encourage workers to use exposure prevention methods, such as washing their hands before eating and taking showers after the completion of work, by providing regular occupational safety training.

Pelatihan Kewirausahaan Digital Untuk Atlet Kota Semarang

This service activity aims to increase the potential of athletes in the world of digital entrepreneurship for athletes assisted by KONI Semarang City through entrepreneurship training in the digital field. The potential for digital businesses in Indonesia is very large and is expected to continue to grow. Therefore the main focus of this activity is to provide a better understanding of the potential outside the world of sport as well as the digital entrepreneurial skills needed to start and manage your own business. The service method is in the form of understanding the Entrepreneurial Mindset, Facilitating Business Logo Creation and Digital Entrepreneurship Skills. By involving expertise in entrepreneurship and training, this program is designed to empower athletes to create independent economic opportunities. Through this approach, it is hoped that athletes can overcome economic challenges after retiring from their sports careers and create a brighter future as successful digital entrepreneurs.

ForametCeTera, a novel CT scan dataset to expedite classification research of (non-)foraminifera

This paper introduces ForametCeTera, a pioneering dataset designed to address the challenges associated with automating the analysis of benthic foraminifera in sediment cores. Foraminifera are sensitive sentinels of environmental change and are a crucial component of carbonate-denominated ecosystems, such as coral reefs. Studying their prevalence and characteristics is imperative in understanding climate change. However, analysis of foraminifera contained in core samples currently requires washing, sieving and manual quantification. These methods are thus time-consuming and require trained experts. To overcome these limitations, we propose an alternative workflow utilizing 3D X-ray computational tomography (CT) for fully automated analysis, saving time and resources. Despite recent advancements in automation, a crucial lack of methods persists for segmenting and classifying individual foraminifera from 3D scans. In response, we present ForametCeTera, a diverse dataset featuring 436 3D CT scans of individual foraminifera and non-foraminiferan material following a high-throughput scanning workflow. ForametCeTera serves as a foundational resource for generating synthetic digital core samples, facilitating the development of segmentation and classification methods of entire core sample CT scans.

Injury and illness surveillance monitoring in team sports: a framework for all

BackgroundSport-related injuries and illnesses can negatively impact athlete welfare at all standards of participation in team sports. Injury and illness surveillance (IIS), and the development of monitoring systems, initiates the sequence of injury and illness prevention. Operational IIS monitoring systems help to appraise epidemiological estimates of injury and illness incidence and burden in various athlete populations. However, the methodological underpinnings of various monitoring systems are not harmonized or widely documented, with the presence of efficient and successful programmes rarely showcased at non-elite levels. The aim is to provide a framework that guides the development of IIS, which will enhance overall surveillance, to indirectly inform injury prevention strategies.MethodsThe process involved all members of the research group initially discussing the research gaps, scope of the project, and the aims of the article. Unique experiences were shared, and specific and global challenges and barriers to IIS at all standards of team sport participation were identified. A tiered system of data collection with corresponding content were produced, with experiences and guidance provided throughout the article.ResultsThe literature has been reviewed and using first-hand experience in conducting IIS programmes in complex and diverse sport settings, the authors have identified key enablers and barriers for best practise as time, technological and human resources, reporter/practitioner training, and medical expertise. Areas of greatest importance regarding the conducting of IIS have been outlined, providing guidance and recommendations across all levels of team sport participation. These areas include definitions, data context, collection procedures, handling, security, ethics, storage, dissemination, quality, compliance, and analysis. Given the barriers to IIS, 3-tiered levels of data collection and content have been proposed. The levels indicate data collection variables, with a focus on sufficiency and achievability, aiming to support the successful conducting of IIS in team sports across all standards of participation. Future opportunities in IIS have been discussed, with several predictive measures and analytical techniques expanded upon.ConclusionsThe framework provides universal guidance for implementing IIS monitoring systems, facilitating athletes, coaches, parents/guardians, governing bodies and practitioners to implement IIS processes, identify challenges, complete analysis, and interpret outcomes at all standards of participation.

Automated Lugano Metabolic Response Assessment in 18F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on 18F-Fluorodeoxyglucose-Positron Emission Tomography.

Artificial intelligence can reduce the time used by physicians on radiological assessments. For 18F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic. Here, we present a deep learning-based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier: NCT01287741) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers NCT02257567, NCT02500407; 20% holdout in ClinicalTrials.gov identifier NCT01287741), outputs the detected lesions at baseline and follow-up to enable focused radiologist review. The method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers NCT01287741, NCT02500407, and NCT02257567, respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers NCT01287741, NCT02500407, and NCT02257567, compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses. These results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.

Establishing consensus on nutrition competencies for medicine: a Delphi study

BackgroundSignificant research, regulatory bodies and even governmental resolutions have identified meaningful nutrition education for medical and other healthcare professionals as a priority. Doctors are well placed to provide nutrition care,...

Publications Among Pain Medicine Fellowship-Trained American Board of Anesthesiology Diplomates.

American Board of Anesthesiology (ABA) diplomates who pursue clinical fellowship training in pain medicine may be better suited to lead scholarly projects and serve as first authors of publications in peer-reviewed journals given their additional training and clinical expertise. The primary aim of this study was to determine whether ABA certification in pain medicine is associated with a greater number of peer-reviewed publications. The secondary aim included assessments of whether pain medicine fellowship training is associated with a higher publication rate (publications per year) or publication in a larger number of peer-reviewed journals. A literature search was conducted in December 2023 using the Scopus database for publications related to anesthesiology and pain medicine in the United States between 2013 and 2023. First authors identified through the search were then individually searched within the ABA physician directory. The following data were collected: author name and identification number, year of publication, publication type (article or review), year of primary anesthesiology certification, and year of fellowship, if applicable. This study identified 9,612 publications and 6,924 unique first authors. Pain medicine fellowship training was associated with a statistically significant increase (p-value < 0.001) in the number of publications (0.546; 95% confidence interval {CI}, 0.386-0.707), publications per year (0.140; 95% CI, 0.121-0.159), and publication in a larger number of peer-reviewed journals (0.256; 95% CI, 0.182-0.330) in regression models adjusted for the number of years from certification. This query of the Scopus database and ABA physician directory indicates that pain medicine fellowship training is associated with statistically significant increases in research productivity, as defined by the number of publications, publications per year, or the number of publications in peer-reviewed journals. However, these increases in research output would not lead to a marked increase inscholarship productivity to justify pursuing a fellowship for this purpose.

Artificial intelligence applications in solar energy

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

Congenital fetal malformations: The challenges of diagnosis and management in a resource-poor nation – the role of ignorance: A case report

Regular ante-natal clinic attendance is routine and recommended for every pregnant mother worldwide. Antenatal care services are usually made easily accessible to all pregnant women and offered in most centres within or near communities. This is to encourage all would-be mothers to access the opportunity of being reviewed by trained experts when pregnant. During such visits, necessary routine investigations are carried out and the results are reviewed and acted upon by the midwife/physician. One such investigation is the obstetric ultrasound which should be done at least two times before delivery. This may not always be possible in a poor resource region like ours. Even where feasible, ignorance may militate against its use. So, invariably, many fetal congenital malformations are missed antenatally and diagnosis is only made at birth. Such was the case of Mrs XX. Unfortunately, even after delivery of the malformed baby ignorance still prevailed against proper management of the newborn.

Atherogenic index of plasma as a predictor of coronary artery disease: a cohort study in south of Iran

BackgroundCoronary artery disease (CAD) is an atherosclerotic disease of an inflammatory nature. Previous studies examining the relationship between triglycerides and high-density lipoprotein cholesterol have highlighted the importance of plasma atherogenic index (AIP) as an important predictor of coronary heart disease. However, due to the lack of adequate information on this topic, this study aimed to investigate the relationship between AIP and coronary heart disease risk.ResultsThis study included 2,226 women and 1,690 men aged 35–70 years who participated in the Bandar Kong Cohort study and met the eligibility criteria. The data was collected using a checklist and questionnaires, which were designed by experienced individuals. After participants completed a registration form and gave informed consent, face-to-face interviews were conducted by trained experts. The validity and reliability of the questionnaire had been verified by the national cohort team prior to its use. The Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1400.171) approved the study. Data from the initial cohort survey using SPSS software version 25, were analyzed to include several factors, including age, sex, smoking status, body mass index (BMI), physical activity level, socioeconomic status, AIP, systolic blood pressure, and diastolic blood pressure. The prevalence of coronary heart disease was found to be 7.5% higher in people with a BMI of 25 or higher. Also, Individuals with low physical activity had a higher prevalence. Individuals with CAD had significantly higher mean values for the AIP, age, systolic blood pressure, and diastolic blood pressure (0.46, 57.50, 128.43, and 81.10, respectively) compared to those without CAD. Furthermore, patients with CAD had lower years of education (2649.45 and 3.59) than individuals without CAD (P < 0.05). Importantly, our findings showed that AIP increased the odds ratio of coronary heart disease by 1.86 as an independent risk factor.ConclusionsBased on our investigation, the AIP is a valuable and independent predictive risk factor for coronary artery disease. This index can be utilized effectively due to its accessibility and affordability, making it a promising tool for risk assessment in clinical settings.

Engagement with language in study abroad: accounts from Korean English-major students in the United States

ABSTRACT This study investigates the emergence and nature of engagement with language during short-term study abroad (STSA). Ten teacher-education students from the same sending university in South Korea participated in a 4-week programme hosted by a U.S. university, with emphasis on multiculturalism and English as a medium of classroom instruction. Adopting the framework of engagement with language and a case study method, the current study focuses on three English majors with a professional stake and expert training as focal cases and their responses to focus group interviews and individual interviews, as well as written reflections. The data reveal the participants’ critical reflections on their prior L2 practices and a complex interplay of cognitive (breakdowns of English and its forms and functions), affective (confidence boosting), and social (interactions for learning) engagement with language in relation to what STSA affords as a context and how the participants negotiate and shape that context. The study further uncovers dynamic interactions of the different forms of engagement, such as juxtaposition of cognitive and affective forms and interconnected forms of cognitive, affective, and social engagement. The findings shed new insight into the complex nature of engagement with language during STSA.

Update to the essential genomic nursing competencies and outcome indicators.

Genomic healthcare applications have relevance to all healthcare professionals including nursing, and most evidence-based clinical applications impact the quality and safety of healthcare. To guide nursing genomic competency initiatives, the Essential Nursing Competencies and Curricula Guidelines for Genetics and Genomics were established through a process of consensus in 2005. A 2009 update incorporated outcome indicators consisting of specific areas of knowledge and clinical performance indicators, to help support academic integration. Almost 20 years have elapsed since these competencies were first established, yet incorporating the competencies into general and specialty scope and standards of nursing practice is inconsistent, competency integration into curricula is highly uneven, continuing education in genomics for nurses is limited, and the genomic capacity of the nursing workforce remains low. These deficits have persisted despite substantial advances in genomic technology which substantially reduced costs and increased evidence-based clinical applications, including direct to consumer genomic tests, the integration of genomics into evidence-based guidelines, and evidence that genomics impacts the quality and safety of healthcare. The aim of this project was to update and achieve consensus on genomic competencies applicable to all registered nurses. This was a mixed methods study. The update to the competencies was performed based first on a literature review to update the competencies based on the current state of the evidence. Using the updated content, a modified Delphi study was conducted with registered nurse panelists from clinical, academic, and research settings. Once consensus was achieved, the competencies were made available through the American Nurses Association for public comment. Public comments were then reviewed and integrated as needed. The literature review resulted in a transition from genetics to genomics, given the reduction in costs, which resulted in an expansion of the scope of testing in both the germline and somatic contexts. Two Delphi rounds were required to reach consensus prior to the public comment period. Public comments were solicited through the American Nurses Association, and each comment was reviewed by the authors and addressed as indicated. The Essentials of Genomic Nursing: Competencies and Outcome Indicators constitute the minimum competency in genomics required of all registered nurses regardless of the level of academic training, role, or specialty. Evidence-based genomic applications span the entire healthcare continuum and, therefore, are relevant for all registered nurses regardless of academic training, role, practice setting, or clinical expertise. These competencies serve as the guide for the minimum requirements for registered nurse practice as well as guide curricula and continuing education for all registered nurses, including but not limited to administrators, educators, nursing leaders, practicing nurses, and researchers.

Hub and Spokes-Implementation of Basic Palliative Care into University Hospital Practice-A Pilot Study.

Background: The aim is to implement knowledge of basic palliative care in selected departments by the Hub and Spokes model. Methods: Implementation of basic palliative care was designed as a stepwise training model by skills lectures over a time period of 2 years. In each of the six selected oncological and nononcological departments, one physician and two nurses were trained in semi-annual half-day meetings as expert representatives within their departments. Results: Semi-structured interviews were conducted to assess implementation outcomes with 12 nurses and 6 physicians. Overall acceptability was high for all departments and professions. Feasibility was given in all departments. Adoption and penetration of a trained expert representative differed between medical and nursing professions. Implementation was more appropriate in the stationary sector. Implementation costs were low. Conclusion: Expansion of the system into a second follow-up period, including more departments, is planned to ensure sustainability.

A Postdoctoral Training Program in Bioimage Analysis.

We herein describe a postdoctoral training program designed to train biologists with microscopy experience in bioimage analysis. We detail the rationale behind the program, the various components of the training program, and outcomes in terms of works produced and the career effects on past participants. We analyze the results of an anonymous survey distributed to past and present participants, indicating overall high value of all 12 rated aspects of the program, but significant heterogeneity in which aspects were most important to each participant. Finally, we propose this model as a template for other programs which may want to train experts in professional skill sets, and discuss the important considerations when running such a program. We believe that such programs can have extremely positive impact for both the trainees themselves and the broader scientific community.