Objective Performance Metrics Research Articles

Identifying vehicle characteristic target based on subjective ratings from drivers

In virtual tire development, defining vehicle characteristic target is crucial to achieving the desired vehicle performance with the equipped tires. Vehicle performance is generally evaluated through subjective ratings by skilled drivers following SAE J1441 guidelines, as well as the objective performance metrics obtained via vehicle dynamics analysis. However, due to the scarcity and bias in real-world testing dataset, the vehicle characteristic target cannot be determined definitively. To address this issue, we propose a recommendation system that searches the unexplored vehicle characteristic domains. This suggested method provides virtual subjective ratings to enrich the limited real-world dataset from the skilled drivers. Moreover, we highlight the correlation between subjective and objective data using vehicle and tire dynamics simulation software. Based on this augmented data, we predict the vehicle characteristic target according to the subjective rating requirements. The proposed approach was validated through various experiments involving skilled drivers and various vehicle-tire combinations, showing 91.26% to 96.2% consistency in generated steering parameter domains. Domains of stability parameters show that one fewer domain was generated by a conservative driver compared to more lenient driver, which confirms the robustness of the proposed approach in accommodating individual driver preferences.

Contrastive Speaker Representation Learning with Hard Negative Sampling for Speaker Recognition.

Speaker recognition is a technology that identifies the speaker in an input utterance by extracting speaker-distinguishable features from the speech signal. Speaker recognition is used for system security and authentication; therefore, it is crucial to extract unique features of the speaker to achieve high recognition rates. Representative methods for extracting these features include a classification approach, or utilizing contrastive learning to learn the speaker relationship between representations and then using embeddings extracted from a specific layer of the model. This paper introduces a framework for developing robust speaker recognition models through contrastive learning. This approach aims to minimize the similarity to hard negative samples-those that are genuine negatives, but have extremely similar features to the positives, leading to potential mistaken. Specifically, our proposed method trains the model by estimating hard negative samples within a mini-batch during contrastive learning, and then utilizes a cross-attention mechanism to determine speaker agreement for pairs of utterances. To demonstrate the effectiveness of our proposed method, we compared the performance of a deep learning model trained with a conventional loss function utilized in speaker recognition with that of a deep learning model trained using our proposed method, as measured by the equal error rate (EER), an objective performance metric. Our results indicate that when trained with the voxceleb2 dataset, the proposed method achieved an EER of 0.98% on the voxceleb1-E dataset and 1.84% on the voxceleb1-H dataset.

Identifying novel metrics of operative performance in surgical training: protocol for a Delphi consensus study

Abstract Introduction: Acquiring the technical and non-technical skills necessary to perform operations safely requires time and dedication. The learning curve is steep and regular feedback on operative performance is needed to help trainees identify strengths and development needs. Traditional methods of assessment are time consuming, prone to bias and lack reliability and scalability. Advances in digital technology in surgery have allowed for more objective measures of operative performance to be generated. Objective performance metrics can provide insights void of opinion and can complement and strengthen traditional methods of feedback. This protocol outlines a study that aims to reach consensus on which performance metrics are most relevant to surgical trainees and how they should be applied in training. Methods and analysis: A three-round electronic Delphi study will be used. Participants will be asked to rate the importance of statements relating to objective performance metrics in surgical training and the ways in which they could be applied. Statements will be created from extensive searches of the literature, discussion with experts and through a pilot. A panel consisting of trainees, trainers, researchers and institutions will be formed to participate in the study. Ethics and dissemination: Formal ethical approval has been granted for this study. Dissemination will be at surgical and education meetings, in addition to publication following peer review.

Automated task-level autonomy assessment in robotic surgery.

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. 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. 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. 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.

The Linkage Between Knowledge Management Practices and Sustainable Business Growth: Empirical Evidence from Nigeria

Purpose: The objective of this study is to examine the correlation between knowledge management (KM) and lasting company expansion within a specific group of family enterprises located in Lagos State, Nigeria. The study encompasses an examination of six distinct sub-variables, namely knowledge sharing, knowledge training, knowledge capture, knowledge transfer, knowledge acquisition, and knowledge application, in relation to the variable of knowledge management. Additionally, it considers four dependent sub-variables, namely profitability, number of employees, customer base, and market share, in relation to the variable of sustainable business growth. Methods: A survey research methodology was employed in this study to examine the correlation between knowledge management and business performance among a specific group of family companies located in Lagos State, Nigeria. This article employed a cross-sectional survey research design and utilised a quantitative technique. This design was adopted based on the research topic and its associated research questions. This paper utilised the previous research methodology employed by Olubiyi, Egwakhe, Amos, and Ajayi (2019), Olubiyi (2019), Olubiyi, Lawal, & Adeoye (2022), Olubiyi (2022), and Olubiyi, Jubril, Sojinu, and Ngari (2022). The study employed a cross-sectional survey approach and focused on a population of family businesses, specifically owners/managers of selected small and medium-sized enterprises (SMEs) in Lagos State, Nigeria. Results and Conclusion: The findings is consistent with the conceptual framework and these studies (Fulgence, Hu, Larbi-Siaw, Tuo, & Gnahe, 2022; Ge & Campopiano, 2021; Ha, Lo, Suaidi, Mohamad,& Razak,2021; Hernández‐Perlines, Moreno‐García, & Yáñez‐Araque, 2017; Inkinen, 2016; Majid, Mahmud, 2019; Marija, 2022; Olubiyi, 2020; Patwary, Alwi, Rehman, Rabiul, Babatunde, & Alam, 2022; Santoro, Messeni-Petruzzelli, & Del Giudice, 2021; Shafique, Kalyar, Patwary, Alwi, Rehman, Rabiul, Babatunde, & Alam, 2022; Shafique, Kianto, & Beh, 2022; Su, & Daspit, 2021; Tan, Hii,. & Cheong, 2022; Torabi, & El-Den, 2017; Zamfir, 2022). Furthermore, the findings of the research validate the empirical data about the correlation between knowledge management strategies and the long-term viability of family enterprises. Evidence suggests that the growth of enterprises in Lagos State, Nigeria is significantly influenced by several factors such as knowledge training, knowledge generation, knowledge capture, information sharing, knowledge transfer, and knowledge application. The data analysis demonstrated a significant correlation between knowledge management techniques and the long-term survival of family firms (R2=0.252, F(6, 461)=27.167; p<0.05). Research implication: The report acknowledges that its findings and implications are specific to Lagos, Nigeria, with a primary focus on family enterprises. Hence, the research might be further extended by conducting comparable studies on major corporations in Nigeria or other regions. In order to enhance the generalizability and reliability of the findings, it is advisable to increase the sample size by incorporating a larger number of respondents through the use of new data collection methods. The primary obstacle in getting objective performance metrics in the majority of family businesses in Lagos State, Nigeria, was the lack of data. Consequently, non-financial performance indicators were employed as a solution. Originality/value: The objective of this study is to examine the correlation between knowledge management (KM) and sustainable business growth within a specific group of family companies located in Lagos State, Nigeria. This study enhances the understanding of knowledge management strategies employed by owner-managers in family businesses, making it relevant to entrepreneurs, policymakers, and scholars. The research presents an innovative analysis of current sources, specifically focusing on the primary metrics employed to assess the knowledge management and sustainable business expansion of family enterprises. This study addresses the want for enhanced lucidity and comprehension within the family business industry.

Empirical Wavelet Transform Based ECG Signal Filtering Method

The electrocardiogram (ECG) is a diagnostic tool that provides insights into the heart’s electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, Liouville–Weyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWT’s strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.

Smart haptic gloves for virtual reality surgery simulation: a pilot study on external ventricular drain training.

Smart haptic gloves are a new technology emerging in Virtual Reality (VR) with a promise to enhance sensory feedback in VR. This paper presents one of the first attempts to explore its application to surgical training for neurosurgery trainees using VR-based surgery simulators. We develop and evaluate a surgical simulator for External Ventricular Drain Placement (EVD), a common procedure in the field of neurosurgery. Haptic gloves are used in combination with a VR environment to augment the experience of burr hole placement, and flexible catheter manipulation. The simulator was integrated into the training curriculum at the 2022 Canadian Neurosurgery Rookie Bootcamp. Thirty neurosurgery residents used the simulator where objective performance metrics and subjective experience scores were acquired. We provide the details of the simulator development, as well as the user study results and draw conclusions on the benefits added by the haptic gloves and future directions.

Examining the Design Actions and Reasoning Factors that Impact Design Performance

Abstract Engineers often do not select the best designs available to them. This research investigates whether specific design actions improve performance in a design exploration task and whether the reasoning factors underpinning these actions can be inferred directly. This study quantitatively evaluates multiple dimensions of design behavior and cognition within concept selection using objective performance metrics. Fifty-six participants were tasked with identifying an optimal design for the gripping contact of a dishwashing robot. Results identified specific design actions that correlated with improved design performance, including exploring fewer design alternatives and isolating parameters. We found that reasoning factors stated by participants did not accurately map onto their observed actions and did not correlate with task performance. Implications related to future computational design support tools are discussed.

Comparative Analysis of Public Transportation Comfort in Addis Ababa: Objective and Subjective Performance Metrics

Passenger comfort significantly influences commuter satisfaction and mode preferences in urban transportation systems. To maintain competitiveness and attract more passengers, conducting continuous performance evaluations based on key performance indicators is commendable. Therefore, this study is aimed at conducting a mode‐wide comfort performance evaluation and comparisons to identify the strengths and weaknesses of respective mass transport alternatives found in Addis Ababa, Ethiopia. The study employs a comprehensive approach by integrating both subjective surveys and objective measurements on key comfort determinants, including in‐vehicle passenger loading, travel time, vibration, and sound, across five transport modes: minibus taxis, midi buses, PSETSE, Anbesa City Bus Service Enterprise (ACBE), and Sheger Mass Transport Enterprise (SMTE). Analysis of variance (ANOVA) reveals variations among transport modes, with minibus taxis emerging as the most comfortable, featuring a travel time of 1.7 min/km, 2.6 min/km during off‐peak and peak times of the day, respectively, and an average of 1.13 passengers per seat in both periods of the day. In contrast, midi buses are identified as the least comfortable, characterized by elevated noise production (77.46 and 80 dB) and an average vibration rating of 3.9 and 4.2 at peak and off‐peak periods of the day. Following midi buses, the ACBE exhibits the highest passenger loading and travel time (2.113 and 1.548 passengers per seat and takes 4.2 and 3.34 min) to cover 1 km during peak and off‐peak periods, respectively. The holistic evaluation demonstrates an average correlation of 81.75%, indicating a strong alignment between peak‐hour objective and subjective assessments. This could help commuters make relevant mode choice decisions and transport companies make market‐oriented decisions in this competitive environment.

The relationship between accounting-based relative performance evaluation (RPE) and CEO compensation

This paper explores the relationship between Accounting-Based Relative Performance Evaluation (RPE) and CEO compensation within the context of corporate governance. The theoretical foundations for RPE are agency theory, economic theory, and stakeholder theory, and the study explores the complexities of CEO compensation, incorporating frameworks like agency theory, tournament theory, and human capital theory. Employing a qualitative descriptive approach, the paper analyzes existing literature to unravel the applications, benefits, and challenges of RPE in CEO compensation. The results and discussion section scrutinizes the RPE process, emphasizing peer benchmarking, defining performance metrics, quantifying relative performance, adjusting for external factors, and incorporating risk and effort sharing. Accounting-Based RPE emerges as a critical subset, offering financial alignment, objective performance metrics, and a shareholder value focus. The paper acknowledges challenges, including data accuracy, comparability issues, and concerns about short-termism. Furthermore, it underscores the integral role of corporate governance in shaping the impact of RPE on CEO compensation.

Augmented reality for surgical skills training, update on the topic

Augmented reality (AR) combines digital information with physical reality, allowing users to interact with virtual data in their real environment. In medical education, this technology is attractive because it allows access to information without taking your eyes off the surgical field. The integration of surgical simulators in resident training offers flexible practice without direct supervision, with benefits such as objective performance evaluation, practice of unusual procedures, and the development of non-technical skills. In addition, simulators are useful for teaching new techniques to experts. In summary, AR and simulators offer valuable opportunities to improve surgical training. The objective of this review is to update the current state of augmented reality in surgical training. The use of augmented reality as an assessment tool in surgical training presents interesting perspectives that deserve consideration. AR can provide objective performance metrics by measuring the technical competency of surgical trainees, whether in a simulated operating environment or in real-world situations. This offers the opportunity to address subjective variability and potential bias in current assessment methods, which often rely on supervisor observation and rating. However, despite these advantages, there has not yet been a comprehensive review to evaluate the use of AR in surgical training. The cost-benefit and implications for data management have not yet been addressed.

Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models

Abstract While GCM horizontal resolution has received the majority of scale improvements in recent years, ample evidence suggests that a model’s vertical resolution exerts a strong control on its ability to accurately simulate the physics of the marine boundary layer. Here we show that, regardless of parameter tuning, the ability of a single-column model (SCM) to simulate the subtropical marine boundary layer improves when its vertical resolution is improved. We introduce a novel objective tuning technique to optimize the parameters of an SCM against profiles of temperature and moisture and their turbulent fluxes, horizontal winds, cloud water, and rainwater from large-eddy simulations (LES). We use this method to identify optimal parameters for simulating marine stratocumulus and shallow cumulus. The novel tuning method utilizes an objective performance metric that accounts for the uncertainty in the LES output, including the covariability between model variables. Optimization is performed independently for different vertical grid spacings and value of time step, ranging from coarse scales often used in current global models (120 m, 180 s) to fine scales often used in parameterization development and large-eddy simulations (10 m, 15 s). Uncertainty-weighted disagreement between the SCM and LES decreases by a factor of ∼5 when vertical grid spacing is improved from 120 to 10 m, with time step reductions being of secondary importance. Model performance is shown to converge at a vertical grid spacing of 20 m, with further refinements to 10 m leading to little further improvement. Significance Statement In successive generations of computer models that simulate Earth’s atmosphere, improvements have been mainly accomplished by reducing the horizontal sizes of discretized grid boxes, while the vertical grid spacing has seen comparatively lesser refinements. Here we advocate for additional attention to be paid to the number of vertical layers in these models, especially in the model layers closest to Earth’s surface where climatologically important marine stratocumulus and shallow cumulus clouds reside. Our experiments show that the ability of a one-dimensional model to represent physical processes important to these clouds is strongly dependent on the model’s vertical grid spacing.

Hybrid is here to stay: Critical behaviors for success in the new world of work

Remote and hybrid working has become the norm globally. This research synthesizes findings from three studies which aim to explore the behaviors that predict success in a remote work environment, to help employees, managers, and organizations better understand how to work and manage a remote workforce. The predictive validity of nine behaviors for success in a remote working environment across three studies were investigated. Study 1 examines self-ratings of both job performance and affective outcomes from a general population sample (N = 1179). Study 2 investigates manager ratings of job performance in an organizational sample (N = 175). Study 3 explores objective performance metrics, before and after the transition to remote working, in two roles from a second organizational sample (N = 531 and 146). The abilities to manage own time and deliver work on schedule, concentrate on tasks over a long period of time without getting distracted, adapt to change, and work well without supervision are the strongest and most consistent predictors of job performance and affective outcomes across studies. This paper provides practical implications of supporting and sustaining a remote-ready workforce by identifying these behaviors that employees can target for development in any remote role.

Applying objective metrics to neurosurgical skill development with simulation and spaced repetition learning.

Surgical skills laboratories augment educational training by deepening one's understanding of anatomy and allowing the safe practice of technical skills. Novel, high-fidelity, cadaver-free simulators provide an opportunity to increase access to skills laboratory training. The neurosurgical field has historically evaluated skill by subjective assessment or outcome measures, as opposed to process measures with objective, quantitative indicators of technical skill and progression. The authors conducted a pilot training module with spaced repetition learning concepts to evaluate its feasibility and impact on proficiency. The 6-week module used a simulator of a pterional approach representing skull, dura mater, cranial nerves, and arteries (UpSurgeOn S.r.l.). Neurosurgery residents at an academic tertiary hospital completed a video-recorded baseline examination, performing supraorbital and pterional craniotomies, dural opening, suturing, and anatomical identification under a microscope. Participation in the full 6-week module was voluntary, which precluded randomizing by class year. The intervention group participated in four additional faculty-guided trainings. In the 6th week, all residents (intervention and control) repeated the initial examination with video recording. Videos were evaluated by three neurosurgical attendings who were not affiliated with the institution and who were blinded to participant grouping and year. Scores were assigned via Global Rating Scales (GRSs) and Task-based Specific Checklists (TSCs) previously built for craniotomy (cGRS, cTSC) and microsurgical exploration (mGRS, mTSC). Fifteen residents participated (8 intervention, 7 control). The intervention group included a greater number of junior residents (postgraduate years 1-3; 7/8) compared to the control group (1/7). External evaluators had internal consistency within 0.5% (kappa probability > Z of 0.00001). The total average time improved by 5:42 minutes (p < 0.003; intervention, 6:05, p = 0.07; control, 5:15, p = 0.001). The intervention group began with lower scores in all categories and surpassed the comparison group in cGRS (10.93 to 13.6/16) and cTSC (4.0 to 7.4/10). Percent improvements for the intervention group were cGRS 25% (p = 0.02), cTSC 84% (p = 0.002), mGRS 18% (p = 0.003), and mTSC 52% (p = 0.037). For controls, improvements were cGRS 4% (p = 0.19), cTSC 0.0% (p > 0.99), mGRS 6% (p = 0.07), and mTSC 31% (p = 0.029). Participants who underwent a 6-week simulation course showed significant objective improvement in technical indicators, particularly individuals who were early in their training. Small, nonrandomized grouping limits generalizability regarding degree of impact; however, introducing objective performance metrics during spaced repetition simulation would undoubtedly improve training. A larger multiinstitutional randomized controlled study will help elucidate the value of this educational method.

Dimensionless plate height is unsuitable for comparing chromatographic columns

Dimensionless plate height (h) is differently defined for the columns with different type of internal filling (internal support structure). It is h = H/d for open columns (d is the internal diameter), h = H/dp for packed columns (dp is the particle size), h = H/ddom (ddom is the domain size) for columns with other filling (e.g., monolithic, pillar array). The plate height (H) and the column kinetic performance in general depend on the flow channels through the column filling – on their cross-sectional dimensions (dchan), shape, and wall porosity. Other than through their effect on the flow channels, the dimensions of skeleton of the filling and the ddom do not affect H. Parameters d, dp and ddom cannot be found from external measurements of a column as a ”black box” separation device and, therefore, are unmeasurable subjective metrics, and, therefore, so is h. Moreover, in some fillings (e.g., monolithic, pillar array) ddom can be chosen independently of dchan. By increasing ddom without changing dchan, one can make h as small as desired with no effect on H and on the column kinetic performance in general. This implies that h does not represent column performance and cannot be used for comparing differently structured column.In this report, previously introduced measurable (objective) performance metrics – the kinetic performance factor (q) and the quality factor (qmax) are described and evaluated. The latter is suitable for comparing differently structured columns. A column practically achievable performance depends not only on qmax, but on the largest pressure that a column can tolerate, the narrowest flow channels that can be manufactured and other practical factors. Practically achievable performance of several column types is compared in this report.

Environmental Intervention Evaluation to Support Motor Planning of Wheelchair Movements: A Pilot Study

Environmental interventions provide a promising opportunity to support inclusive mobility in our rapidly changing society and workforce. Concurrently, the trends for aging into mobility disability, working into older age, and health and economic burdens of unpaid caregiving continue to rise. Hence, providing equitable mobility for all is an urgent need. In the context of manual wheelchair control, a novel method investigating the impact of environmental interventions on individual’s internal representation of motor actions is proposed. In addition to measuring objective and subjective performance metrics, we propose that an understanding of how internal representations of tasks are influenced by user perception of the environment has promising potential towards informing effective and inclusive task-environment interactions. The application explored in this pilot study aims to facilitate mobility.

Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot-assisted radical prostatectomy using our validated hydrogel-based simulation platform and identify potential metrics of surgical expertise. Video, robotic kinematics, and force sensor data were collected from 35 board-certified urologists at the 2022 American Urological Association conference. Video was annotated for surgical gestures. Objective performance indicators were derived from robotic system kinematic data. Force metrics were calculated from hydrogel model integrated sensors. Data were fitted to 3 supervised machine learning models-logistic regression, support vector machine, and k-nearest neighbors-which were used to predict procedure-specific learning curve proficiency. Recursive feature elimination was used to optimize the best performing model. Logistic regression predicted caseload with the highest AUC score for 5/7 possible data combinations (force, 64%; objective performance indicators + gestures, 94%; objective performance indicators + force, 90%; gestures + force, 93%; objective performance indicators + gestures + force, 94%). Support vector machine predicted the highest AUC score for objective performance indicators (82%) and gestures (94%). Logistic regression with recursive feature elimination was the most effective model reaching 96% AUC in predicting case-specific experience. Most contributory features were identified across all model types. We have created a machine learning-based algorithm utilizing a novel combination of objective performance indicators, gesture analysis, and integrated force metrics to predict surgical experience, capable of discriminating between surgeons with low or high robot-assisted radical prostatectomy caseload with 96% AUC in a standardized, simulation-based environment.

Active control time: an objective performance metric for trainee participation in robotic surgery.

Trainee participation and progression in robotic general surgery remain poorly defined. Computer-assisted technology offers the potential to provide and track objective performance metrics. In this study, we aimed to validate the use of a novel metric-active control time (ACT)-for assessing trainee participation in robotic-assisted cases. Performance data from da Vinci Surgical Systems was retrospectively analyzed for all robotic cases involving trainees with a single minimally invasive surgeon over 10months. The primary outcome metric was percent ACT-the amount of trainee console time spent in active system manipulations over total active time from both consoles. Kruskal-Wallis and Mann-Whitney U statistical tests were applied in analyses. A total of 123 robotic cases with 18 general surgery residents and 1 fellow were included. Of these, 56 were categorized as complex. Median %ACT was statistically different between trainee levels for all case types taken in aggregate (PGY1s 3.0% [IQR 2-14%], PGY3s 32% [IQR 27-66%], PGY4s 42% [IQR 26-52%], PGY5s 50% [IQR 28-70%], and fellow 61% [IQR 41-85%], p = < 0.0001). When stratified by complexity, median %ACT was higher in standard versus complex cases for PGY5 (60% vs. 36%, p = 0.0002) and fellow groups (74% vs. 47%, p = 0.0045). In this study, we demonstrated an increase in %ACT with trainee level and with standard versus complex robotic cases. These findings are consistent with hypotheses, providing validity evidence for ACT as an objective measurement of trainee participation in robotic-assisted cases. Future studies will aim to define task-specific ACT to guide further robotic training and performance assessments.

A pain in the neck: prototyping and testing of a patient simulator neck for spinal immobilization training

This paper presents the development and user-testing of a novel concept for patient simulators, aiming to enhance spinal immobilization training. Iterative prototype interactions with critical stakeholders revealed a need for evidence-based guidelines and training for suspected neck injuries. A realistic and compliant neck prototype with sensor feedback was developed to address the need for objective performance metrics. The conceptual prototype was used in an experimental study (n = 12) to obtain subjective and objective feedback on its characteristics and use in medical training. In the experiment, users were asked to perform spinal immobilization techniques on a simulator while sensor data recorded head and neck movements. Furthermore, a Likert-scale questionnaire and subjective feedback were gathered. Results are used to discuss proposed performance metrics and whether they can be used as quality performance indicators for formative and summative training feedback. The results also suggest the neck prototype to realistically simulate an unconscious patient regarding the obtained range of motion and spinal compliance.

To assess the impact of augmented reality training on improving laparoscopic vaginal vault closure using objective performance metrics

To assess the impact of augmented reality training on improving laparoscopic vaginal vault closure using objective performance metrics