Objective Assessment Of Performance Research Articles

진로체험지원센터 이용이 학생의 진로체험활동 참여에 미치는 영향 분석

This study empirically analyzed the effectiveness of Career Experience Support Centers, which are operated nationwide. Established to support students' participation in career experience activities within their communities, 223 centers were in operation as of June 2022. The primary function of these centers is to support schools in facilitating career experience activities by identifying career experience sites within the community and connecting them with schools. This study aimed to verify the effective operation of these Career Experience Support Centers by examining whether their use by schools influences students' participation in career experience activities. Data for the analysis were derived from responses of third-grade in middle school students to the 'Survey on the Status of Career Education in Primary and Secondary Schools,' conducted by the Korea Research Institute for Vocational Education and Training (KRIVET). Based on survey responses from students, parents, career guidance teachers, school administrators, and homeroom teachers, school- and student-level variables were identified. To determine whether the use of Career Experience Support Centers by schools and the satisfaction with these centers influence students' participation in career experience activities, a multilevel logistic regression analysis was conducted. The analysis revealed that the use of Career Experience Support Centers by schools has a statistically significant effect on students' participation in career experience activities. Additionally, satisfaction with the centers positively impacts students' participation. It was found that the likelihood of students engaging in career experience activities increases by 1.4 times with the use of the centers and by 1.7 times with high satisfaction. Based on the analysis results, this study suggests enhancing the status and functions of Career Experience Support Centers and calls for an objective assessment of these centers' operational performance.

Enterprise generative artificial intelligence technologies, Internet of Things and blockchain-based fintech management, and digital twin industrial metaverse in the cognitive algorithmic economy

Research background: Enterprise generative AI system-based worker behavior tracking and monitoring, socially responsible organizational practices, employee performance management satisfaction, and human resource management procedures, relationships, and outcomes develop on hiring and objective performance assessment algorithms in terms of human resource management activities, functions, processes, practices, policies, and productivity. Deep reinforcement and machine learning techniques, operational and analytical generative AI and cloud capabilities, and real-time anomalous behavior recognition systems further fintech development for credit and lending services, payment analytics processes, and risk assessment, monitoring, and mitigation. Generative AI tools can bolster predictive analytics by collaborative and interconnected sensor and machine data for tailored, seamless, and fine-tuned product, operational process, and organizational workflow development, efficiency, and innovation, driving agile transformative changes in digital twin industrial metaverse. Purpose of the article: We show that enterprise generative AI-driven schedule prediction tools, job search and algorithmic hiring systems, and synthetic training data can improve team selection, job performance and firing decisions, hiring decision processes, and workforce productivity in terms of prediction and decision-making by use of algorithmic management, system performance, and production process tracking tools. Blockchain-based fintech operations can shape cloud-based financial and digital banking services, quote-to-cash process automation, cash-settled crypto futures, digital loan decisioning, asset tokenization simulated transactions, transaction switching and routing operations, tailored peer-to-peer lending, and proactive credit line management. Collaborative unstructured enterprise data processing, infrastructure, and governance can develop on AI decision and behavior automation technology, retrieval augmented generation and development management systems, and real-time data descriptive and predictive analytics, driving productivity surges and competitive advantage in digital twin industrial metaverse. Methods: Reference and review management tools, together with evidence synthesis screening software, harnessed were Abstrackr, AMSTAR, ASReview Lab, CASP, Catchii, Citationchaser, DistillerSR, JBI SUMARI, Litstream, PICO Portal, and Rayyan. Findings & value added: The current state of the art is improved for theory on organizational issues and for policy making as deep learning-based generative AI tools and workplace monitoring systems can augment performance and productivity, gauge employee effectiveness, build resilient, satisfied, and engaged workforce, assess human capital, skill, and career development, drive employee and productivity expectations in relation to flexibility and stability, and shape turnover, retention, and loyalty. Cloud and account servicing technologies can be deployed in generative AI fintechs for embedded cryptocurrency trading, transaction monitoring and processing, digital asset transfers, payment screening, corporate and retail banking operations, and fraud prevention. Generative AI technologies can reshape jobs and reimagine meaningful work, involving creativity and innovation and adaptable and resilient sustained performance, providing valuable constructive feedback, optimizing workplace flexibility and psychological safety, and measuring and supporting autonomy and flexibility-based efficiency, performance, and productivity, while configuring demanding, engaging, and rewarding experiences by cloud and edge computing devices in digital twin industrial metaverse.

Modified Delphi Procedure to Achieve Consensus for the Concept of a National Curriculum for Minimally Invasive and Robot-assisted Surgery in Germany (GeRMIQ).

The rapid development of minimally invasive surgery (MIS) and robot-assisted surgery (RAS) requires standardized training to ensure high-quality patient care. In Germany, there is currently a lack of a standardized curriculum that teaches these specialized skills. The aim of this study is to find a consensus for the development of a nationwide curriculum for MIS and RAS with the subsequent implementation of the consented content.A modified Delphi process was used to reach consensus among national experts in MIS and RAS. The process included a literature review, an online survey and an expert conference.All 12 invited experts participated in the survey. They primarily achieved consensus on 73% and secondarily within the expert conference on 95 out of 122 questions (77.9%). The preference for a basic curriculum as a foundation on which specialized modules can build on was particularly clear. The results support the development of an integrated curriculum for MIS and RAS that includes step-by-step training from theoretical knowledge via e-learning modules to practical skills in dry lab simulations and in the OR. Emphasis was placed on the need to promote clinical judgment and decision making through targeted assessment during the learning curve to ensure effective application of learned skills in clinical practice. There was also a consensus that training content must be aligned with learners' skill acquisition using objective performance assessments in line with the principle of proficiency-based progression (PBP). The continuous updating of the curriculum to keep it up to date with the latest technology was considered essential.The study underlines the urgent need for a standardized training curriculum for MIS and RAS in Germany in order to increase patient safety and improve the quality of surgical care. There is broad expert consensus for the implementation of such a curriculum. It aims to ensure a contemporary and internationally competitive uniform quality of training and to increase the attractiveness of surgical training.

WP6.14 - Automated analysis of operative video in surgical training: Scoping review

Abstract Aims There is an increasing availability of operative video for use in surgical training. Emerging technologies can now assess video footage and automatically generate metrics that could be harnessed to improve the assessment of operative performance. This scoping review explores the current application of automated video analytics in surgical training. Methods PubMed, Scopus, Web of Science and the Cochrane database were searched to identify studies which applied automated video analytics to trainee-performed operations. Search terms included ‘trainee’, ‘video analytics’ and ‘education’. Two reviewers independently screened and extracted data on the methods of analysis, metrics generated and application to training. The Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Review (PRISMA-ScR) guidelines were followed. Results Thirteen studies were identified. Computer vision tracking was the most common method of video analysis. Metrics were generated for processes (e.g. movement of instruments) and outcomes (e.g. intraoperative phase duration). Two studies generated metrics to assess critical safety elements (e.g. critical view of safety in laparoscopic cholecystectomy). Automated metrics were able to differentiate between different skill levels (e.g. consultant vs. trainee) and correlated with traditional methods of assessment. There was a lack of longitudinal application to training and only one qualitative study reported the experience of trainees using automated video analytics. Conclusion Automated video analytics is an emerging field which provides an objective assessment of operative performance. Gathering evidence to demonstrate the impact of long-term use on training outcomes is a priority for future research.

The Benchmarking Exercise Programme for Older People (BEPOP): Design, Results and Recommendations from The First Wave of Data Collection.

The Benchmarking Exercise Programme for Older People (BEPOP) service improvement project seeks to determine and promote the exercise training characteristics associated with positive outcomes for resistance exercise for older people living with, or at risk of, sarcopenia or physical frailty. Mixed-methods service improvement project. Ten UK National Health Service physiotherapist-led therapy services delivering exercise interventions for older people submitted anonymized data for up to 20 consecutive patients. A multidisciplinary expert panel generated a report and recommendations with site-specific benchmarking data and feedback. In parallel, participating physiotherapy team members were interviewed to elicit feedback on BEPOP rationale, processes and perceived value. Data from 188 patients were included, mean age 80 years (range 60-101). 115 (61%) received objective assessment of strength-based physical performance. Bodyweight exercises (173 [92%]) and resistance bands (49 [26%]) were the commonest exercise modalities. Exercises progressed predominantly through increased repetitions (163 [87%]) rather than increased load. 50 (30%) had no reassessment of outcomes; only 68 (41%) were signposted to follow-on exercise services. Staff interviews identified themes around knowledge, diagnosis, data collection and practice reflection. BEPOP was feasible to deliver and generated actionable insights for service improvement via improved diagnosis, measurement and progression of resistance exercise.

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.

The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance". From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours. Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.

Best Sales Selection Using a Combination of Reciprocal Rank Weighting Method and Multi-Attribute Utility Theory

The best salespeople are individuals who are not only able to meet or exceed sales targets, but also demonstrate exceptional skills in building relationships with customers, understanding their needs, and offering effective solutions. The problem of selecting the best salespeople often involves the challenge of an objective and fair assessment, as diverse evaluation criteria can affect the final result. One of the main obstacles is the presence of subjectivity in judgment, which can arise from personal preferences or pressure to maintain good relationships. This study aims to implement a sales performance evaluation model that combines the Reciprocal Rank Weighting and Multi-Attribute Utility Theory (MAUT) methods to obtain a more accurate and objective assessment of sales performance. This research contributes to the management literature and decision support systems by offering a new approach in sales performance evaluation. This opens up opportunities for further research and practical applications in the field of performance evaluation and salesforce management. Based on the final score calculated using the MAUT method, the salesperson rank from best to lowest is as follows: Sales 7 is ranked top with a value of 0.646, indicating the best overall performance. Sales 3 followed in second place with a value of 0.6125, followed by Sales 9 with a value of 0.5604 in third position.

Independent Fiscal Institutions as a Part of European Traditions

Fiscal councils are independent public institutions aiming to promote the sustainability of public finances through various functions, such as assessing budgetary plans and their implementation or preparing budgetary and macroeconomic forecasts. Thus, synthesising the attempts made in the literature to define the concept of independent fiscal institutions, it should be emphasised that by providing an independent, impartial and objective assessment of fiscal policy and performance, they promote sound fiscal decisions and sustainable public finances. Independent fiscal institutions also reduce information asymmetries and promote transparency in public finances, thereby raising the reputation and electoral costs for governments that pursue imprudent policies or breach key commitments. As such, they can support contemporary states in addressing the tendency to increase spending and deficits. To better understand the contemporary factual and normative state of the analysed dissertation topic, it is worth tracing, even in some summary, the process of emergence and the directions of development and evolution of independent fiscal institutions. Although independent fiscal institutions are relatively new bodies in the political architecture of the vast majority of the EU Member States, a small group of countries pioneered the creation of these institutions. It is necessary to trace the systemic solutions that have determined the legal and constitutional status of fiscal institutions in recent decades in order to understand the factors that have led to their reassessment.

THE ROLE OF HUMAN RESOURCE MANAGEMENT IN IMPROVING THE PERFORMANCE OF LOMBO VILLAGE APPARATUS, SIDENRENG RAPPANG DISTRICT

This research aims to identify the role of human resource management (HRM) in improving the performance of village officials in Lombo Village, Sidenreng Rappang Regency. The research method used is a qualitative approach with descriptive case studies. Primary data was collected through interviews with village government officials, while secondary data was obtained from various sources such as websites, social media, books, and presentation materials. Literature reviews on HRM and performance were used as the basis for data analysis. The research findings indicate that HRM plays a crucial role in enhancing the performance of village officials. Careful selection of village officials involving community participation, employee development through training and education, and providing fair and adequate compensation are some key aspects identified. Based on the research findings, it is recommended to continue improving the selection process of village officials with greater precision and transparency, continuing investment in employee development, implementing periodic and objective performance assessment systems, and reviewing and updating existing compensation systems. It is hoped that the implementation of these recommendations will enhance the effectiveness of HRM at the village level, which in turn will contribute to improving the performance of village officials and village development overall.

Use of optical see-through head-mounted devices in dentistry - a scoping review.

The aim of this scoping review was to identify the scientific evidence related to the utilization of Optical See- Through Head-Mounted Display (OST-HMD) in dentistry, and to determine future research needs. The research question was formulated using the "Population" (P), "Concept" (Cpt), and "Context" (Cxt) framework for scoping reviews. Existing literature was designated as P, OST-HMD as Cpt, and Dentistry as Cxt. An electronic search was conducted in PubMed, Embase, Web of Science, and CENTRAL. Two authors independently screened titles and abstracts and performed the full-text analysis. The search identified 286 titles after removing duplicates. Nine studies, involving 138 participants and 1760 performed tests were included in this scoping review. Seven of the articles were preclinical studies, one was a survey, and one was a clinical trial. The included manuscripts covered various dental fields: three studies in orthodontics, two in oral surgery, two in conservative dentistry, one in general dentistry, and the remaining one in prosthodontics. Five articles focused on educational purposes. Two brands of OST-HMD were used: in eight studies HoloLens Microsoft was used, while Google Glass was utilized in one article. The overall number of included studies was low; therefore, the available data from this review cannot yet support an evidence-based recommendation for the clinical use of OST-HMDs. However, the existing preclinical data indicate a significant capacity for clinical and educational implementation. Further adoption of these devices will facilitate more reliable and objective quality and performance assessments, as well as more direct comparisons with conventional workflows. More clinical studies must be conducted to substantiate the potential benefits and reliability for patients and clinicians.

“Seeing inside out”: revealing the effectiveness of otoscopy training in virtual reality enhanced practical exams - a randomized controlled trial

BackgroundThe study aimed to assess the impact of different training modalities on otoscopy performance during a practical exam using a high-fidelity simulator and to determine if objective evaluation of otoscopy is feasible using a simulator that records insertion depth and tympanic membrane coverage.MethodsParticipants were assigned to one of four groups: control and three intervention groups with varying training approaches. Participants received otoscopy training and then were assessed through a practical exam on a high-fidelity simulator that uses virtual reality to visualize the ear canal and middle ear. Performance was evaluated using a modified Objective Structured Assessment of Technical Skills checklist and Integrated Procedural Performance Instrument checklist. Insertion depth, tympanic membrane coverage, and correct diagnosis were recorded. Data were tested for normal distribution using the Shapiro-Wilk test. One-way ANOVA and, for non-normally distributed data, Kruskal-Wallis test combined with Dunn’s test for multiple comparisons were used. Interrater reliability was assessed using Cohen’s κ and Intraclass correlation coefficient.ResultsAll groups rated their training sessions positively. Performance on the OSATS checklist was similar among groups. IPPI scores indicated comparable patient handling skills. The feedback group examined larger tympanic membrane areas and had higher rates of correct diagnosis. The correct insertion depth was rarely achieved by all participants. Interrater reliability for OSATS was strong. IPPI reliability showed good correlation.ConclusionRegardless of training modality, participants perceived learning improvement and skill acquisition. Feedback improved examination performance, indicating simulator-guided training enhances skills. High-fidelity simulator usage in exams provides an objective assessment of performance.

Machine learning in employee performance evaluation: A HRM perspective

This research explores how machine learning, the tech whiz kid, is shaking up the traditional HR world of employee reviews. Companies are hungry for better ways to assess their workforce, and machine learning comes to the table with a buffet of data analysis tools. Productivity metrics, project wins, even that qualitative feedback, all get crunched by these algorithms to dish up comprehensive and objective performance pictures. This research dives into both the pros and cons of bringing this tech titan into HR, aiming to make evaluations not just accurate, but also fair and impactful. It's about finding the sweet spot where technology and human understanding join forces to power up performance reviews for the modern workplace. In the ever-evolving dance of the modern workplace, where performance reigns supreme, the old waltz of subjective evaluations stumbles toward obsolescence. This study steps into the spotlight, exploring the transformative potential of machine learning as the new lead partner in HRM, specifically for crafting nuanced and objective performance assessments. Imagine algorithms like virtuoso musicians, weaving together diverse data melodies – productivity's staccato riffs, project outcomes' triumphant crescendos, even the subtle whispers of qualitative feedback – to paint a vibrant portrait of individual contributions. But this data-driven tango isn't without its tricky steps. This research scrutinizes both the grace and potential missteps of machine learning in HRM, aiming to illuminate a path toward optimized performance evaluations that are not only accurate but also fair and effective. By bridging the gap between technology and human understanding, this study offers a roadmap for organizations waltzing towards a future where performance appraisals unlock the full potential of their workforce.

New generation of passenger vehicles: FCV or HEV?

This paper compares the performance and parameter characteristics of Fuel Cell Vehicles (FCV) and Hybrid Electric Vehicles (HEV) with a view towards an objective assessment of the relative performance of these vehicles. Firstly, the main characteristics of hybrid electric vehicles (HEVs) as low emission vehicles (LEVs), including presumed high efficiency is considered. Then, comparisons for well-to-wheels emissions for various vehicles are presented. Well-to-wheels efficiencies, emissions, and fuel economy are also compared for FCVs and HEVs. In addition, other issues like battery types for HEVs and HFCVs are explored in this paper. The potential control strategies for FCVs and HEVs will be discussed and compared. In both FCVs and HEVs, best control strategies need to rely on predicting the driver command, which presents a particularly challenging opportunity for further development. Finally, this paper gives the comparison of total costs for FCVs and HEVs.

NextGen Training for Medical First Responders: Advancing Mass-Casualty Incident Preparedness through Mixed Reality Technology

Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.

Analisa Penerapan Metode WASPAS Dalam Sistem Pendukung Keputusan Penilaian Kinerja Checker Terbaik Pada Kereta Api

Performance assessment in a work environment involving monitoring and checking activities plays an important role in maintaining quality and efficiency. This study analyzes the application of the Weighted Aggregated Sum Product Assessment (WASPAS) method as part of the Decision Support System in the process of evaluating the performance of the best checkers. The WASPAS method is used to address the complexity of measuring checker performance by considering various relevant criteria. This article describes the steps for implementing the WASPAS method and its integration into a decision support system. The results of the analysis show that this approach can help improve accuracy and objectivity in performance appraisal, providing a strong basis for management to make informed decisions regarding the selection of the best checkers. In this study, checker performance data were collected and analyzed carefully. The results of the analysis provide insight into a more accurate and objective checker performance assessment. The integration of the WASPAS method into DSS helps managers or decision makers overcome subjectivity in the assessment process, as well as avoid potential bias that could affect the final result. From the results of the study, it can be concluded that the application of the WASPAS method in SPK can increase the validity and reliability of checker performance assessments. This approach allows managers to carry out a more comprehensive and balanced evaluation of every aspect of checker performance. However, careful selection of criterion weights and sensitivity to changes in weights is required to ensure optimal results. Thus, this study provides a more in-depth look at how the WASPAS method can be applied in the DSS context for assessing the performance of the best checkers. The implications can be felt in making decisions that are more informed and accurate in managing individual performance in the work environment. From the results of calculations that have been carried out from 7 alternative Checker data using the WASPAS Method, the best alternative, namely the fifth alternative with the name Riski Sudrajat, obtains the highest score, namely 0.939. Therefore the fifth alternative was chosen as the Checker with the best performance on the Railway.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control.

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Transformasi Digital terhadap Sumber Daya Manusia sebagai Upaya Meningkatkan Kapabilitas Perusahaan

Technological and industrial developments require qualified human resources in every company, strong companies are able to survive all forms of change and challenges of the times. Currently, companies must be able to anticipate and adapt to technological developments and one of the most important assets of a company is its human resources because this affects the survival of the company. The results of the literature review show that human resources have a significant influence on digital transformation, especially through increasing company capabilities. By using an HR quality assessment based on knowledge, skills, expertise and behavior, organizations can accurately determine how to improve the capabilities of each employee. To support objective performance assessment, a balance of qualified human resources is needed, and resource support will facilitate objective performance assessment. In the digital human resource transformation process, the optimization of a company's human resource capacity is measured through the knowledge, skills, expertise and behavior of the company's employees. By paying attention to the impact of performance, businesses can design strategies to achieve goals supported by digital transformation. This research presents an objective view of how to improve a company's human resource capabilities through digital transformation. Focusing on the digital era, this article provides knowledge about the importance of more accurate and effective decision making in human resource management. The aim of this article is to provide an effective combination to improve a company's HR capabilities during this complex digital transformation.

Enhanced Learning Using Digital Recordings with Integrated Standardized Patient Examinations (ISPEs) in Physical Therapy Education: A Qualitative Pilot Study

(1) Background: Integrated standardized patient examinations (ISPEs) allow students to demonstrate competence with curricular learning and communication. Digital recordings of these experiences provide an objective permanent record, allowing students to review and improve their performance. Although recordings have been utilized as a tool in physical therapy education, no studies have described the impact of reviewing recordings of ISPE. This qualitative pilot study aimed to investigate student perceptions and learning after reviewing their recordings of ISPE. (2) Methods: Second-year Doctor of Physical Therapy students (n= 23) participated in the study by completing an anonymous online survey after reviewing their recordings from three ISPEs. Thematic analysis was used to identify codes and central themes from the survey data. (3) Results: The results showed that 95.6% of students found the video review process beneficial. Five themes emerged: (i) digital recordings provide an objective performance assessment, (ii) approaches to self-review vary, (iii) it provides an opportunity for growth, (iv) a holistic review is possible, and (v) students need structure and guidance in the process. (4) Conclusions: Study findings indicate that a review of the recordings of ISPEs facilitates the development of clinical skills for physical therapy students. Implementing an explicit framework for reviewing the recordings may enhance the process and facilitate further promotion of reflection-on-action.

828 Real-Time Artificial Intelligence Instruction in Comparison to Human Expert Instruction in Surgical Technical Skills Teaching – a Randomized Controlled Trial

Abstract Aim Surgical simulators equipped with artificial intelligence (AI) systems provide risk-free training on realistically simulated complex patient cases, objective performance assessment, and tailored error avoidance feedback. This study compared the efficacy of real-time AI instruction with human instructor-mediated training in surgical technical skills teaching. Method Medical students (n = 98) were randomly allocated into three feedback groups. Everyone repeated a virtual brain tumour resection five times. The first repetition was done without receiving feedback to assess their baseline performance. Students in Group 1 did not receive real-time feedback, and they were shown visual feedback only after each procedure based on expert benchmarks. Students in Group 2 were instructed in real-time by the AI system. After each task, the students were shown their error-video clips generated by this system alongside the expert-level demonstrations relating to each error. Students in Group 3 were instructed by human instructors during the tasks. After each task, instructors summarized the areas of improvement and demonstrated correction techniques. All participant data was scored using a composite-score. Statistical analysis was conducted to compare learning from the first to the last task repetition. Results Students in Group 2 and Group 3 significantly improved in the performance score by the third and second repetition, respectively (p<0.01, p = 0.01) compared to their baseline performance. Students in Group 2 achieved significantly higher scores than those in Group 3 in the fifth repetition (p<0.01). Conclusions Artificial intelligence systems may provide competency-based curricula in surgical training with efficient surgical technical skills teaching methodologies.

Periodontal disease and sports performance: a systematic review and meta-analysis

ABSTRACT This systematic review and meta-analysis aimed to assess the association between athletic performance and periodontal disease (PD). Observational studies published up to April 2022 were searched in online databases (PubMed, Scopus, Web of Science, The Cochrane Library, LILACS, OpenGrey, and Google Scholar). PECO statement focused on studies with professional athletes (Participants) with PD (Exposition) compared to those with healthy periodontium (Comparison) and its effects on sports performance (outcome). Searches were conducted according to the PRISMA guidelines (registration protocol: 10.17605/OSF.IO/7ADH6) and risk of bias evaluation according to the Joanna Briggs Institute checklist. A total of 793 references were retrieved, of which eight were considered eligible. Two studies did not show an association between clinical PD parameters and performance. A meta-analysis with 396 participants suggested a relationship between PD and self-reported reduction in sports performance (odds ratio [OR] = 1.55; 95% confidence interval [CI] 1.04–2.31; p = 0.03; I2 = 0%) with moderate quality of evidence accordingly GRADE analysis. Despite the differences in performance evaluations between studies, PD was associated with reduced self-reported sports performance. Additional studies assessing confounding factors and objective assessment of PD and performance are needed to clarify the real effect of both conditions.