From wearables to performance: how acceptance of IoT devices influences physical education results in college students.

With the advent of IoT technology in education, understanding its impact on physical education is crucial. This study investigates how the acceptance of wearable IoT devices influences the physical education results of college freshmen. It posits that user acceptance plays a mediating role in the effectiveness of these devices in enhancing physical performance metrics. The study enrolled 150 first-year students from Guangzhou University of Finance, divided equally into an experimental group and a control group. Participants in the experimental group were provided with 'Xiaomi 8' smart bracelets to be worn during physical education classes. The study spanned six weeks, focusing on assessing various physical performance metrics and the acceptance of the wearable technology. The data analysis involved comparing the physical performance of both groups and conducting regression analyses to evaluate the mediation effect of acceptance. Results indicated significant improvements in physical performance metrics in the experimental group, as evidenced by the Standardized Mean Differences (SMD). Notably, enhancements were observed in short-distance speed and aerobic endurance. The direct impact of wearable IoT devices on physical performance accounted for 66.4% variance, which increased to 84.1% upon incorporating acceptance as a mediator. These findings suggest that the effectiveness of wearable technology in physical education is significantly influenced by students' acceptance. The study concludes that wearable IoT devices can effectively enhance physical education outcomes among college students, with user acceptance playing a crucial mediating role. This underscores the importance of considering user acceptance in the integration of technology in educational settings. The findings provide valuable insights for educators and technologists in designing and implementing technology-integrated curricula.

Deepfake: definitions, performance metrics and standards, datasets, and a meta-review.

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term "deepfake." Based on both the research literature and resources in English, this paper gives a comprehensive overview of deepfake, covering multiple important aspects of this emerging concept, including (1) different definitions, (2) commonly used performance metrics and standards, and (3) deepfake-related datasets. In addition, the paper also reports a meta-review of 15 selected deepfake-related survey papers published since 2020, focusing not only on the mentioned aspects but also on the analysis of key challenges and recommendations. We believe that this paper is the most comprehensive review of deepfake in terms of the aspects covered.

Exploring the dynamics of Women's field hockey performance: A comparative analysis of the impact of opponent quality between Olympic Games and Chinese National Games.

This is an observational retrospective study analyzed the performance of the Chinese women's national field hockey team during the 2020 Tokyo Olympics and 2021 National Games to assess the impact of opposition quality on performance. Game statistics were collected using notational analysis software for 76 Olympic and 40 National Games matches. Non-parametric Mann-Whitney U tests were used to compare tournaments. No significant differences existed for 35 out of 38 metrics, except Offense in 25-Yd Area (P = 0.013), Handball Style (P = 0.000) and Entry into Arc - Right Lane (P = 0.017). When exclusively considering Chinese national team's observations, superior National Games performance did emerge for Shot (P = 0.046), Goal from Short Corner (P = 0.044), Into the Arc (P = 0.046), Entry into Arc - Q3 (P = 0.009), Dribble into the Arc (P = 0.046), Handball Style into Arc (P = 0.041), Forehand Shot (P = 0.033), and Small Skill Shot (P = 0.014). The study underscores the influence of opposition quality on team performance, with a need for targeted improvements in arc penetration efficacy, conversion rates of shots to goals, and adaptation of tactical approaches against stronger defenses. The research points towards the need for strategic high-performance programs, improved domestic league quality, and a structured youth development system to elevate the overall standard of Chinese field hockey to achieve global competitiveness.

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower's distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower's distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

Automated task-level autonomy assessment in robotic surgery.

INTRODUCTION: Surgical autonomy for trainees has remained elusive to quantify. Proportion of active control time (ACT) of a trainee during a robotic case can be used as a broad measure of autonomy. However, this metric lacks in the granular detail of quantifying at what specific steps trainees were actively participating. We aim to quantify trainee involvement during robotic-assisted hiatal hernia repair at a task-specific level. METHODS: We performed a retrospective review of surgical performance data from robotic-assisted hiatal hernia repairs performed. These cases were segmented into 5 tasks by AI-assisted annotation with human review. The segmented tasks included: hiatal dissection, gastric fundus mobilization, mediastinal dissection, cruroplasty and fundoplication. Tasks were excluded if video segmentation of tasks was incorrect. During each task, ACT was recorded for resident, fellow and attending. Resident and fellow ACT per task was compared using the Mann-Whitney U test. RESULTS: Residents had the highest %ACT in the hiatal dissection (53%), gastric fundus mobilization (84%) and fundoplication (57%) tasks. Fellows had greater than 80% ACT in all 5 tasks, with the highest %ACT in the gastric fundus mobilization (100%) and hiatal dissection (88%). There was a significant difference between resident and fellow ACT during mediastinal dissection and cruroplasty. CONCLUSIONS: This study demonstrates how objective performance metrics and automated case segmentation can quantify trainee participation at a task-specific level. By utilizing data afforded by a robotic surgery platform, we are able to provide an objective and automated form of assessment with minimal impact on the workflow of attendings and residents. Our findings can serve to inform residents on what steps they can expect to be involved in during the procedure, appropriate to their PGY level. With this task-level data, we can provide a roadmap for trainee progression to achieve full surgical autonomy.

An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images.

As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.

Factors Affecting the Running Performance of Soccer Teams in the Turkish Super League.

Performance analysis in sports is a rapidly evolving field, where academics and applied performance analysts work together to improve coaches' decision making through the use of performance indicators (PIs). This study aimed to provide a comprehensive analysis of factors affecting running performance (RP) in soccer teams, focusing on low (LI), medium (MI), and high-speed distances (HI) and the number of high-speed runs (NHI). Data were collected from 185 matches in the Turkish first division's 2021-2022 season using InStat Fitness's optical tracking technology. Four linear mixed-model analyses were conducted on the RP metrics with fixed factors, including location, team quality, opponent quality, ball possession, high-press, counterattacks, number of central defenders, and number of central forwards. The findings indicate that high-press and opponent team quality affect MI (d = 0.311, d = 0.214) and HI (d = 0.303, d = 0.207); team quality influences MI (d = 0.632); location and counterattacks impact HI (d = 0.228, d = 0.450); high-press and the number of central defenders affects NHI (d = 0.404, d = 0.319); and ball possession affects LI (d = 0.287). The number of central forwards did not influence any RP metrics. This study provides valuable insights into the factors influencing RP in soccer, highlighting the complex interactions between formations and physical, technical-tactical, and contextual variables. Understanding these dynamics can help coaches and analysts optimize team performance and strategic decision making.

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers.

Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.

COVID-19's influence on Karachi stock exchange: A comparative machine learning algorithms study for forecasting.

The COVID-19 pandemic has great effects for economies internationally. This study studies the interconnection between COVID-19 metrics and Pakistan's premier stock exchange, the Karachi Stock Exchange (KSE) with the object of identifying the most effective machine learning (ML) model for predicting KSE developments in the pandemic. Our investigation periods the peak COVID-19 period from March 1, 2020, to November 26, 2021, applying data from both the KSE 100 index and COVID-19 associated variables. Five various ML methods were applied involving Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Regression Tree (Rtree), and Support Vector Machine (SVM) and measured their performance employing critical accuracy metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The outcomes discover that the RF model outperformed its equivalents realizing an R2 of 0.91 with k = 5. These results conflict with a previous study that supported a negative impact of COVID-19 on improved stock markets. The visions from this study can assist investors in managing strategic investment decisions and assist policymakers in making measures to reduce the pandemic's effects on the stock market.

What the Eyes Don't See: An Objective Assessment of Players' Contribution to Team Success in Men's Rugby League.

Purpose: This study addresses the lack of objective player-based metrics in men's rugby league by introducing a comprehensive set of novel performance metrics designed to quantify a player's overall contribution to team success. Methods: Player match performance data were captured by Stats Perform for every National Rugby League season from 2018 until 2022; a total of five seasons. The dataset was divided into offensive and defensive variables and further split according to player position. Five machine learning algorithms (Principal Component Regression, Lasso Regression, Random Forest, Regression Tree, and Extreme Gradient Boost) were considered in the analysis, which ultimately generated Wins Created and Losses Created for offensive and defensive performance, respectively. These two metrics were combined to create a final metric of Net Wins Added. The validity of these player performance metrics against traditional objective and subjective measures of performance in rugby league were evaluated. Results: The metrics correctly predicted the winner of 80.9% of matches, as well as predicting the number of team wins per season with an RMSE of 1.9. The metrics displayed moderate agreement (Gwet AC1 = 0.505) when predicting team of the year award recipients. When predicting State of Origin selection, the metrics displayed moderate agreement for New South Wales (0.450) and substantial agreement for Queensland (0.652). Conclusion: The development and validation of these objective player performance metrics represent significant potential to enhance talent evaluation and player recruitment.

Machine Learning Models and Applications for Early Detection.

From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

What Are Doctors For? A Call for Compassion-Based Metrics as a Measure of Physician Value.

Modern measures of physician value are couched in terms of productivity, volume, finance, outcomes, cure rates, and acquisition of an increasingly vast knowledge base. This inherently feeds burnout and imposter syndrome as physicians experience an inability to measure up to unrealistic standards set externally and perceived internally. Ancient and modern wisdom suggests that where populations fail to flourish, at root is a failure to grasp a vision or true purpose. Traditional philosophical conceptions of a physician's purpose center around compassion, empathy, and humanism, which are a key to thwarting burnout and recovering professional satisfaction. New compassion-based metrics are urgently needed and will positively impact physician well-being and improve population health.

Automated performance metrics and surgical gestures: two methods for assessment of technical skills in robotic surgery.

The objective of this study is to compare automated performance metrics (APM) and surgical gestures for technical skills assessment during simulated robot-assisted radical prostatectomy (RARP). Ten novices and six experienced RARP surgeons performed simulated RARPs on the RobotiX Mentor (Surgical Science, Sweden). Simulator APM were automatically recorded, and surgical videos were manually annotated with five types of surgical gestures. The consequences of the pass/fail levels, which were based on contrasting groups' methods, were compared for APM and surgical gestures. Intra-class correlation coefficient (ICC) analysis and a Bland-Altman plot were used to explore the correlation between APM and surgical gestures. Pass/fail levels for both APM and surgical gesture could fully distinguish between the skill levels of the surgeons with a specificity and sensitivity of 100%. The overall ICC (one-way, random) was 0.70 (95% CI: 0.34-0.88), showing moderate agreement between the methods. The Bland-Altman plot showed a high agreement between the two methods for assessing experienced surgeons but disagreed on the novice surgeons' skill level. APM and surgical gestures could both fully distinguish between novices and experienced surgeons in a simulated setting. Both methods of analyzing technical skills have their advantages and disadvantages and, as of now, those are only to a limited extent available in the clinical setting. The development of assessment methods in a simulated setting enables testing before implementing it in a clinical setting.

Impact of Age of Heart Transplant Program on Patient Survival and Post-Transplant Outcomes.

BACKGROUND: The relationship between age of a heart transplant (HT) program and outcomes has not been explored. METHODS: We performed a retrospective cohort analysis of the United Network for Organ Sharing database of all adult HTs between 2009 and 2019. For each patient, we created a variable that corresponded to program age: new (<5), developing (≥5 but <10) and established (≥10) years. RESULTS: Of 20 997 HTs, 822 were at new, 908 at developing, and 19 267 at established programs. Patients at new programs were significantly more likely to have history of cigarette smoking, ischemic cardiomyopathy, and prior sternotomy. These programs were less likely to accept organs from older donors and those with a history of hypertension or cigarette use. As compared to patients at new programs, transplant patients at established programs had less frequent rates of treated rejection during the index hospitalization (HR 0.43 [95% CI, 0.36-0.53] p < 0.001) and at 1 year (HR 0.58 [95% CI, 0.49-0.70], p < 0.001), less frequently required pacemaker implantations (HR 0.50 [95% CI, 0.36-0.69], p < 0.001), and less frequently required dialysis (HR 0.66 [95% CI, 0.53-0.82], p < 0.001). However, there were no significant differences in short- or long-term survival between the groups (log-rank p = 0.24). CONCLUSION: Patient and donor selection differed between new, developing, and established HT programs but had equivalent survival. New programs had increased likelihood of treated rejection, pacemaker implantation, and need for dialysis. Standardized post-transplant practices may help to minimize this variation and ensure optimal outcomes for all patients.

Association Between Utilization of Services and Perioperative Outcomes for Lung Cancer Resection.

INTRODUCTION: Lung cancer resection has largely focused on perioperative outcomes (eg, mortality) to benchmark performance. While variations in perioperative outcomes and in utilization of services (eg, ambulatory procedures, hospitalization) have been independently demonstrated, there has been limited evaluation of associations between these outcomes. We evaluated the association between perioperative outcomes and utilization of services to evaluate provider performance across a broader context of care. PATIENTS AND METHODS: This was a retrospective cohort study of patients undergoing lung cancer resection in 2017 to 2019. We utilized hierarchical logistic regression models to determine risk- and reliability-adjusted mortality and risk-adjusted utilization of services, at the hospital-level. We then evaluated utilization of services across quartiles of perioperative mortality. RESULTS: A total of 15,168 patients across 297 hospitals underwent lung cancer resection. Mean risk- and reliability-adjusted 90-day mortality varied between 1.58% (95% CI, 1.54%-1.62%) and 2.74% (95% CI, 2.59%-2.90%) across quartiles. Risk-adjusted utilization of all ambulatory procedures was highest in the best performing (lowest mortality) quartile at 37.7% (95% CI, 33.6%-41.8%). Additionally, risk-adjusted inpatient utilization prior to and after surgery was lowest in the best performing quartile at 15% (95% CI, 13.7%-16.3%) and 19.3% (95% CI, 17.5%-21.0%), respectively. CONCLUSIONS: Hospitals with the lowest perioperative mortality demonstrated trends towards using more outpatient resources prior to surgery, but fewer inpatient services surrounding lung cancer resection. This correlation highlights the importance of incorporating utilization of services in addition to other metrics to profile the efficiency and effectiveness of centers performing lung cancer resection across a broader spectrum of care.

Conversion of an outpatient pharmacy to a mail-order pharmacy within a health system.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: The establishment of a new outpatient pharmacy provided a strategic opportunity to repurpose and convert an existing outpatient pharmacy into a closed-door mail-order pharmacy within a health system. This article describes the steps taken to successfully make this change and evaluates the impact. SUMMARY: The mail-order pharmacy conversion project was divided into 3 phases: phase 1 (before conversion) from July through August 2022, phase 2 (conversion) from October through November 2022, and phase 3 (after conversion) from December 2022 through February 2023. Phase 1 included standardizing workflows with standard operating procedure (SOP) development, improving automation, determining staffing ratios, gathering baseline staff engagement data, and identifying primary and secondary outcomes of interest. Phase 2 encompassed SOP implementation and training of mail-order team members. Phase 3 involved evaluating available pharmacy floorspace, marketing mail-order services, and the second distribution of the staff engagement survey. The measured outcomes of this project were total prescription volumes, increase in total revenue, and staff engagement. Data collection was completed in phase 3. CONCLUSION: The existing outpatient pharmacy was successfully converted to a closed-door pharmacy, and the associated prescription volume increased. Developing a strategic action plan to establish SOPs, calculate staffing performance metrics, and identify opportunities for growth and engaging frontline team members were essential to the success of this project.

Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology: A Review of Basic Elements.

Artificial intelligence (AI) is currently being used as a routine tool for day-to-day activity. Medicine is not an exception to the growing usage of AI in various scientific fields. Vascular and interventional neurology deal with diseases that require early diagnosis and appropriate intervention, which are crucial to saving patients' lives. In these settings, AI can be an extra pair of hands for physicians or in conditions where there is a shortage of clinical experts. In this article, the authors have reviewed the common metrics used in interpreting the performance of models and common algorithms used in this field.

A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.

International consensus panel for transurethral resection of bladder tumours metrics: assessment of face and content validity.

OBJECTIVE: To develop performance metrics that objectively define a reference approach to a transurethral resection of bladder tumours (TURBT) procedure, seek consensus on the performance metrics from a group of international experts. METHODS: The characterisation of a reference approach to a TURBT procedure was performed by identifying phases and explicitly defined procedure events (i.e., steps, errors, and critical errors). An international panel of experienced urologists (i.e., Delphi panel) was then assembled to scrutinise the metrics using a modified Delphi process. Based on the panel's feedback, the proposed metrics could be edited, supplemented, or deleted. A voting process was conducted to establish the consensus level on the metrics. Consensus was defined as the panel majority (i.e., >80%) agreeing that the metric definitions were accurate and acceptable. The number of metric units before and after the Delphi meeting were presented. RESULTS: A core metrics group (i.e., characterisation group) deconstructed the TURBT procedure. The reference case was identified as an elective TURBT on a male patient, diagnosed after full diagnostic evaluation with three or fewer bladder tumours of ≤3 cm. The characterisation group identified six procedure phases, 60 procedure steps, 43 errors, and 40 critical errors. The metrics were presented to the Delphi panel which included 15 experts from six countries. After the Delphi, six procedure phases, 63 procedure steps, 47 errors, and 41 critical errors were identified. The Delphi panel achieved a 100% consensus. CONCLUSION: Performance metrics to characterise a reference approach to TURBT were developed and an international panel of experts reached 100% consensus on them. This consensus supports their face and content validity. The metrics can now be used for a proficiency-based progression training curriculum for TURBT.

Predicting cervical cancer risk probabilities using advanced H20 AutoML and local interpretable model-agnostic explanation techniques.

Background: Cancer is positioned as a major disease, particularly for middle-aged people, which remains a global concern that can develop in the form of abnormal growth of body cells at any place in the human body. Cervical cancer, often known as cervix cancer, is cancer present in the female cervix. In the area where the endocervix (upper two-thirds of the cervix) and ectocervix (lower third of the cervix) meet, the majority of cervical cancers begin. Despite an influx of people entering the healthcare industry, the demand for machine learning (ML) specialists has recently outpaced the supply. To close the gap, user-friendly applications, such as H2O, have made significant progress these days. However, traditional ML techniques handle each stage of the process separately; whereas H2O AutoML can automate a major portion of the ML workflow, such as automatic training and tuning of multiple models within a user-defined timeframe. Methods: Thus, novel H2O AutoML with local interpretable model-agnostic explanations (LIME) techniques have been proposed in this research work that enhance the predictability of an ML model in a user-defined timeframe. We herein collected the cervical cancer dataset from the freely available Kaggle repository for our research work. The Stacked Ensembles approach, on the other hand, will automatically train H2O models to create a highly predictive ensemble model that will outperform the AutoML Leaderboard in most instances. The novelty of this research is aimed at training the best model using the AutoML technique that helps in reducing the human effort over traditional ML techniques in less amount of time. Additionally, LIME has been implemented over the H2O AutoML model, to uncover black boxes and to explain every individual prediction in our model. We have evaluated our model performance using the findprediction() function on three different idx values (i.e., 100, 120, and 150) to find the prediction probabilities of two classes for each feature. These experiments have been done in Lenovo core i7 NVidia GeForce 860M GPU laptop in Windows 10 operating system using Python 3.8.3 software on Jupyter 6.4.3 platform. Results: The proposed model resulted in the prediction probabilities depending on the features as 87%, 95%, and 87% for class '0' and 13%, 5%, and 13% for class '1' when idx_value=100, 120, and 150 for the first case; 100% for class '0' and 0% for class '1', when idx_value= 10, 12, and 15 respectively. Additionally, a comparative analysis has been drawn where our proposed model outperforms previous results found in cervical cancer research.