Level Of Expertise Research Articles

Adapting the World Café Method to an Online Format: Insights From Novice Qualitative Researchers

The original World Café (WC), by Brown and Isaac, is a widely used data collection method in qualitative research across various settings and is typically conducted in person. However, during the spring 2020 semester, the onset of the COVID-19 pandemic prompted us to adapt our planned WC research project from an in-person to an online format using Zoom. Two online cafés were hosted with branch campus students of a large Midwest university during October, one in the morning (n=11) and one in the evening (n=7). This methods paper offers a concise overview of the WC method as an innovative approach to qualitative data collection in a branch campus college setting. We outline the original research plan, detail the adjustments made for online cafes, and summarize the outcomes of these virtual sessions. As novice qualitative researchers working with branch campus college students, we draw vital lessons from our experience with the WC. These lessons emphasize the importance of selecting a method suitable for the target audience and researchers’ expertise level. Active participation in one or more WCs before conducting a study proves invaluable, and we highlight the facilitative role of a graphic recorder in ensuring optimal engagement and effective member-checking.

Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method

Background and AimsInflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC). MethodsWe proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs. ResultsIn the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926). ConclusionsComparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.

Development and Acceptability of Provider Training to Increase Treatment Engagement of Parents in Their Children's Behavioral Health Care Need.

BACKGROUND: Disruptive, Impulse-Control, and Conduct disorders (DIC) affect 5 million children in the United States and often require comprehensive and long-term behavioral health care for which sustained parental involvement is essential. Our research team is developing an intervention to improve parental engagement in the behavioral health care of their children with DIC. The intervention, which will be a modification of an evidence-based shared decision-making intervention called DECIDE, will include a parent component and a provider component. AIM: To determine the acceptability of the provider component of the modified DECIDE intervention. METHODS: The provider intervention is an asynchronous self-paced online training program made up of five modules: introduction, shared decision-making, perspective-taking, attributional errors, and being a responsive provider. The training was piloted with 41 providers in two public child and adolescent treatment programs. Following completion of the training, semi-structured interviews were conducted with the providers to assess the acceptability of the training. The interviews were analyzed with conventional content analysis. RESULTS: The provider training was well received by providers, and many had made practice changes based on what they had learned. Several offered recommendations for improvement, most notably the need to tailor the training based on provider role, discipline, and level of expertise. CONCLUSIONS: The feedback given by providers will be used to refine future iterations of the provider training component of the modified DECIDE intervention. Psychiatric nurses and other clinicians may draw from strategies incorporated in the training program to improve parent engagement in the treatment of children with DIC.

Damage control thoracic surgery: What you need to know.

Damage control surgery in trauma prioritizes patient stabilization through an initial temporizing surgical approach to rapidly control hemorrhage and contamination, minimizing intraoperative time to allow for resuscitation and the correction of hypothermia, coagulopathy, and acidosis in the intensive care unit. This is followed by definitive repair of injuries once physiological parameters have improved. While damage control techniques for traumatic intra-abdominal and extremity injuries are well established and frequently utilized, the same cannot be said for damage control thoracic surgery. The complexity of thoracic injuries, the intricate decision making process, the level of surgical expertise required, and potential complications make damage control thoracic surgery particularly challenging. However, advances in surgical techniques, improvements in perioperative care, and the emergence of adjuncts such as extracorporeal membrane oxygenation have significantly enhanced decision making and underscored the importance of timely and decisive intervention in damage control thoracic surgery to optimize patient outcomes. This review aims to provide a comprehensive overview of damage control thoracic surgery, detailing the principles, indications, operative techniques, perioperative management, and the integration of advanced therapies to improve outcomes in patients with severe thoracic injuries.

Explainable predictive process monitoring: a user evaluation

Explainability is motivated by the lack of transparency of black-box machine learning approaches, which do not foster trust and acceptance of machine learning algorithms. This also happens in the predictive process monitoring field, where predictions, obtained by applying machine learning techniques, need to be explained to users, so as to gain their trust and acceptance. In this work, we carry on a user evaluation on explanation approaches for predictive process monitoring aiming at investigating whether and how the explanations provided (i) are understandable; (ii) are useful in decision making tasks; (iii) can be further improved for process analysts with different predictive process monitoring expertise levels. The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for business process management users — with and without experience in predictive process monitoring — differences exist in the comprehension and usage of different plots, as well as in the way users with different predictive process monitoring expertise understand and use them.

Key Competencies in Emergency Medicine Training in Belgium: A New Criteria-Based Evaluation Strategy.

Background In French-speaking Belgium, Emergency Medicine (EM) has been a recognized professional qualification for the last two decades. Currently, there is no consensus on the core competencies required for EM postgraduates. During the six-year training period, the acquisition of technical skills (scientific knowledge and technical procedures) is emphasized, but little account is taken regarding the development of non-technical skills such as communication, collaboration, leadership, and professionalism. Furthermore,currently, there is no criteria-based assessment grid to evaluate the achievement of skills at the end of each three-month clinical rotation. The subjectivity inherent in the current evaluation process increases the inequity and variability from one clinical rotation to another. This study aimed to develop a practical tool: a criteria-based assessment grid to identify the core competencies required of EM postgraduates, help learners identify skills that have been acquired and those not yet attained, and establish an evaluation process that is equitable, objective, and uniform. Methodology Using a group facilitation technique, we developed a consensus among a panel of expert emergency department clinical rotation supervisors from three French-speaking universities. During the first phase of the study, the experts validated a list of core competencies adapted from different existing competency frameworks. The validated core competencies constituted the evaluation criteria for the assessment grid. During the second phase of the study, the experts determined the level of skill expertise expected for each of these competencies, depending on the training level of the EM postgraduate. In our assessment grid, five levels of skill expertise and three levels of EM postgraduate training were defined. Results A total of 18 Emergency department clinical rotation supervisors from the three French-speaking universities participated during the first round of the process, 12 during the second round, and 11 during the third round. Of the 81 initial competencies proposed, 78 reached consensus. For each of these competencies, the level of skill expertise was determined for the three levels of training. Conclusions A criteria-based assessment grid was developed by consensus. The grid identifies the core competencies required of EM postgraduates, integrating technical and non-technical skills. Used at the end of each clinical rotation, it determines which skills have been acquired and where gaps exist, allowing for improvements in EM training. The assessment grid promotes an evaluation that is criteria-based, objective, and uniform from one clinical rotation to another.

An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time‐Distributed 2D CNN‐BLSTM Models, and mRMR Feature Selection

ABSTRACTThe advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time‐consuming. In the last decades, artificial intelligence‐based computer‐aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time‐distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.

Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning.

To propose a deep learning-based approach for predicting the most-fixated regions on optical coherence tomography (OCT) reports using eye tracking data of ophthalmologists, assisting them in finding medically salient image regions. We collected eye tracking data of ophthalmology residents, fellows, and faculty as they viewed OCT reports to detect glaucoma. We used a U-Net model as the deep learning backbone and quantized eye tracking coordinates by dividing the input report into an 11 × 11 grid. The model was trained to predict the grids on which fixations would land in unseen OCT reports. We investigated the contribution of different variables, including the viewer's level of expertise, model architecture, and number of eye gaze patterns included in training. Our approach predicted most-fixated regions in OCT reports with precision of 0.723, recall of 0.562, and f1-score of 0.609. We found that using a grid-based eye tracking structure enabled efficient training and using a U-Net backbone led to the best performance. Our approach has the potential to assist ophthalmologists in diagnosing glaucoma by predicting the most medically salient regions on OCT reports. Our study suggests the value of eye tracking in guiding deep learning algorithms toward informative regions when experts may not be accessible. By suggesting important OCT report regions for a glaucoma diagnosis, our model could aid in medical education and serve as a precursor for self-supervised deep learning approaches to expedite early detection of irreversible vision loss owing to glaucoma.

BFORAMS@MIKROTAX: AN ONLINE DATABASE FOR DEEP-SEA BENTHIC FORAMINIFERAL TAXONOMY

ABSTRACT The bforams@mikrotax database focuses on deep-sea foraminifera from the Cretaceous through Cenozoic, providing a comprehensive, universally accessible taxonomic resource. Based on nannotax and pforams@mikrotax, this database provides an easy-to-use website for experts, researchers, and early career scientists, promoting stability in identifications and nomenclature. By providing detailed descriptions, images, citations, and expert opinions, bforams@mikrotax is becoming a crucial tool for paleoenvironmental, paleoceanographic, paleoclimatic, biostratigraphic, paleobiological studies, and foraminiferal geochemical analyses. The “Main Catalog” provides monographic coverage of 1,600 key taxa, including lists of junior synonyms, updated primary and secondary type images, morphological descriptions, paleobathymetry, and graphical stratigraphic range bars. The “Original Description Catalog” includes information from the original species designations, type specimen repository and locality information, and images of the primary type specimens. The bforams@mikrotax database will provide an invaluable research, reference, and teaching tool for students and researchers across all levels of expertise.

A Deep Learning-Based, Real-Time Image Report System for Linear Endoscopic Ultrasound

Background and aimsThe integrity of image acquisition is critical for biliopancreatic Endoscopic Ultrasonography (EUS) reporting, significantly affecting the quality of EUS examinations and disease-related decision-making. However, the quality of EUS reports varies among endoscopists. To address this, we developed a deep learning-based EUS automatic image reporting system (EUS-AIRS), aiming to achieve automatic photodocumentation in real-time during EUS, including capturing standard stations, lesions, and puncture procedures. MethodsEight deep learning models trained and tested using 235,784 images were integrated to construct the EUS-AIRS. We tested the performance of EUS-AIRS through man-machine comparison at two levels: retrospective test (include internal and external test), and prospective test. From May 2023 to October 2023, 114 patients undergoing EUS at Renmin Hospital of Wuhan University were consecutively recruited for prospective test. The primary outcome was the completeness of the EUS-AIRS for capturing standard stations. ResultsIn terms of completeness in capturing biliopancreatic standard stations, EUS-AIRS exceeds the capabilities of endoscopists at all levels of expertise in retrospective internal (90.8% [95%CI 88.7%-92.9%] vs. 70.5% [95%CI 67.2%-73.8%], p<0.001), and external test (91.4% [95%CI 88.4%-94.4%] vs 68.2% [95%CI 63.3%-73.2%], p<0.001). EUS-AIRS demonstrated high accuracy and completeness in capturing standard station images. The completeness significantly outperformed manual endoscopist reports: 91.4% [95%CI, 89.4% - 93.4%] vs. 78.1% [95%CI, 75.1% - 81.0%), p<0.001. ConclusionsEUS-AIRS exhibits exceptional capabilities in real-time capturing high-quality and high-integrity biliopancreatic EUS images, showcasing the potential of applying an artificial intelligence image reporting system in the EUS field.

Treatment of Geriatric Acetabular Fractures Using the Modified Stoppa Approach: A Review Article of Technique, Tips, and Pitfalls

The surgical treatment of geriatric acetabular fractures is becoming increasingly important due to a demographic change in age. While acetabular fractures used to occur more frequently in younger patients, they are currently more prevalent in geriatric patients. This change has also led to an increase in the frequency of anterior and combined anterior acetabular fractures. Surgery for geriatric acetabular fractures is very challenging, and surgeons need years of experience to be able to deal with the advantages and disadvantages of pelvic surgery. This is why a high level of surgical expertise is required. The aim of this article is to provide an insight into the topic of geriatric acetabular fractures with a critical narrative review of the current literature and a focus on minimally invasive surgical treatment using the modified Stoppa approach without patients’ own assessment. The modified Stoppa approach offers excellent visibility of the anterior structures of the acetabulum and can address the quadrilateral surface effectively. Pelvic surgery, in particular acetabular surgery, offers patients many advantages, such as rapid mobilization and the quick relief of pain symptoms. Total hip arthroplasty is currently being discussed as an alternative with good results for certain types of acetabular fractures in older patients, though it requires clarification of studies and recommendations.

Sociotechnical Factors Affecting Interoperability of Digital Humanities

ABSTRACTIn the past two decades, the digitization of humanities resources has rapidly expanded, giving rise to the field of Digital Humanities (DH). This evolution has fostered a growing need for effective interoperability—the ability to access and integrate data across diverse digital collections, tools, and platforms. Despite significant technological advancements and the development of standards aimed at facilitating interoperability, challenges persist. These challenges are not only technical but are also influenced by sociocultural factors, which include organizational and cultural elements. This paper aims to bridge the gap in the literature concerning these sociotechnical factors affecting interoperability within DH projects and collections.This paper presents the analysis of the real‐world experiences of DH researchers attempting to connect material from different sources. Each participant described a specific incident where combining software or content was particularly problematic. This data was analyzed via thematic analysis. The findings reveal a complex landscape of interoperability challenges, underpinned by a range of sociotechnical factors. These factors include, but are not limited to, discrepancies in metadata standards, differences in organizational cultures, and varying levels of technical expertise among practitioners. By highlighting these factors, the paper contributes valuable insights into the multifaceted nature of interoperability issues in DH, offering evidence‐based plans to address these challenges.

An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

To effectively address the continuously increasing demands of air transport, air traffic management (ATM) systems are evolving towards a human-artificial intelligence (AI) hybrid automation paradigm. In this paradigm, air traffic controllers (ATCOs) play a crucial role in ensuring safe and efficient operations. Recognizing ATCOs’ task capability is essential for evaluating performance, optimizing task assignments, and personalizing training strategies. Physiological signals, such as eye movements, offer objective insights into human behavior and cognitive processes, making them valuable for identifying the task capability of ATCOs. In this study, an integrated framework leveraging machine learning is proposed to achieve this goal. First, a Transformer-attentional long short-term memory (LSTM) network is developed to analyze eye movement patterns, capturing both global and long-term dependencies for precise skill level detection. Next, manual parameters are extracted from the raw eye tracking data and the SHAP (SHapley Additive exPlanation) method is utilized to determine the importance of these parameters, aiding in the selection of relevant performance metrics. Furthermore, a radar chart is implemented to intuitively visualize and compare performance metrics across different skill levels based on the selected parameters. A case study and extensive experiments are conducted to validate the effectiveness of the proposed framework. This research advances task capability recognition for ATCOs in a human-in-the-loop scenario, with a focus on expertise level detection, parameter importance identification, and performance metric comparison.

Standardized Ultrasound Protocol for Peripherally Inserted Central Catheters in Neonates: A Retrospective, X-ray Controlled Observational Study.

Objectives: Ultrasound (US) has been reported to be a reliable imaging modality for locating the tip of peripherally inserted central venous catheters (PICC) in neonates. However, its use requires a certain level of expertise, which may limit its application. Standardization of US examinations using designed protocols may improve their efficiency and ease of use. The objective of our study is to evaluate the effectiveness of introducing a standardized US protocol for PICC guidance. Methods: An expert panel was formed in order to develop a standardized US protocol for PICC assessment. Institutional review board agreement was obtained. This protocol was then used by participating clinicians to assess PICC position. Every assessment was followed by a radiographic control. The study group consisted of 262 US examinations of PICC lines in a level III neonatal intensive care unit (NICU) performed according to the designed protocol. Subsequent statistical analysis was made with the RStudio software, R version 4.3.1 (2023-06-16 ucrt). Results: Compared to the X-ray control, standardized US examinations showed 100% sensitivity, 81% specificity, an accuracy of 98% and a 98% precision for assessing catheter placement. The Cohen's Kappa value for the comparison of X-ray and US studies was 0.88, indicating good agreement between the two methods. Conclusions: Standardized US examinations have shown similar potential for evaluating the location of PICC lines when compared with radiographic studies. Implementing a standardized protocol in the NICU may facilitate the ultrasound assessment of PICC lines and reduce exposure to ionizing radiation.

놀이중심 교육과정의 놀이기록에서 전문적 시각으로 살펴본 교사의 PCK

Objectives This study aims to explore the Pedagogical Content Knowledge (PCK) of teachers in the context of play-based early childhood education. The focus is on how teachers observe, document, and support play activities in educational settings that demand a high level of professional expertise. By analyzing teachers' play documentation from a professional vision perspective, the study seeks to identify what aspects teachers focus on during play situations and how their PCK is manifested. Methods This study analyzed 19 play documentation cases from the 2019 revised Nuri Curriculum to examine teachers' selective attention and knowledge-based reasoning. The analysis of selective attention focused on the specific elements of play that teachers observed, the aspects of children they concentrated on, the areas they emphasized to support and suggest during play, and the experiences and learnings they highlighted. For knowledge-based reasoning, the study assessed teachers' inferential actions based on observational evidence, their professional knowledge, and educational theories. Results First, the analysis revealed that teachers focused on the transformation of play through chance encounters and on all the relationships children form. Second, in the interplay of play and activities, the teachers' PCK was evident in their attuned listening to children's language and behavior while implementing their educational plans, as well as their pedagogical PCK in responding spontaneously to children's needs and supporting their learning. Conclusions The examination of teachers' PCK in play documentation indicated that it is an intertwining of educational knowledge, contextual (practical) knowledge, and knowledge about children. This results in the manifestation of PCK through planning, observation, support, and interpretation by teachers. The study highlighted the importance of teachers’ roles in implementing a play-based curriculum that aims to deepen, expand, and reorganize educational content and experiences to promote higher levels of learning and support.

PATHOEXTRACT: A BIOINFORMATIC PIPELINE FOR QUALITY CONTROL AND HOST DNA REMOVAL IN PLASMODIUM FALCIPARUM NGS DATA

Malaria, caused by Plasmodium falciparum, is a significant global health burden, particularly in sub-Saharan Africa. Deep sequencing (NGS) of parasite genomes has revolutionized our understanding of its biology and the emergence of drug resistance. However, the presence of host human DNA and other microbial contaminants within patient samples can hinder accurate and efficient parasite genome analysis. To address this challenge, we have developed PathoExtract, a robust bioinformatics pipeline that integrates commonly used tools into a streamlined workflow. PathoExtract leverages Snakemake, a workflow management system, to provide a flexible and reproducible framework for data processing. The pipeline incorporates rigorous quality control steps to identify and remove low-quality reads and contaminants. Host DNA and microbial sequences are effectively filtered out using a combination of alignment-based and alignment-free methods, ensuring that only Plasmodium falciparum reads are retained for downstream analysis.The pipeline offers an intuitive graphical user interface, making it accessible to researchers with varying levels of bioinformatics expertise. This user-friendly interface simplifies the process of running the pipeline, even for those unfamiliar with command-line tools. The code and documentation for PathoExtract are freely available at: https://github.com/stanlasso/DREPAL-PATHOEXTRACT.

Exploring hypnotist trance: the experiences of skilled practitioners

ABSTRACT Milton Erickson first conceptualized the hypnotist trance (HT) as a unique psychological state developed in clinicians during hypnosis sessions. This qualitative study aimed to investigates HT through the experiences of 12 skilled Clinician Hypnosis Specialists (CHS). Data were collected via semi-structured face-to-face interviews, exploring participants’ attitudes toward HT, its impact on their practice, and their strategies for developing and regulating it. Thematic analysis revealed that most CHS view HT as enhancing empathy, communication, and therapeutic effectiveness. However, challenges such as time distortion, hypnotic regression, and countertransference issues were also noted. The study highlights HT’s dual nature – offering significant therapeutic benefits while presenting challenges that need careful management. These findings emphasize the importance of comprehensive HT training in hypnotherapy education and advocate for further research to explore HT across diverse contexts and expertise levels to deepen understanding of this complex phenomenon.

Ablation of Supraventricular Arrhythmias With as Low as Reasonably Achievable X-Ray exposure (AALARA): Results of Prospective, Observational, Multicenter, Multinational, Open-Label Registry Study on Real World Data Using Routine Ensite 3D Mapping During SVT Ablation.

The reduction of fluoroscopic exposure during catheter ablation of supraventricular arrhythmias is widely adopted by experienced electrophysiology physicians with a relatively short learning curve and is becoming the standard of care in many parts of the world. While observational studies in the United States and some parts of Western Europe have evaluated the minimal fluoroscopic approach, there are scarce real-world data for this technique and the generalizability of outcomes in other economic regions. The AALARA study is a prospective, observational, multicenter, and multinational open-label study. Patients were recruited from 13 countries across Central Eastern Europe, North and South Africa, the Middle East, and the CIS (Commonwealth of Independent States), with different levels of operator expertise using minimal fluoroscopic exposure techniques. Data on radiation exposure, procedural success, complications, recurrence, and quality of life changes were collected and analyzed. A total of 680 patients were enrolled and followed for 6 months. The majority were ablation naïve with the commonest arrhythmia ablated being typical AVNRT (58%) followed by Atrial Flutter (23%). Zero fluoroscopy exposure was observed in almost 90% of the cases. Fluoroscopy was most commonly used during the ablation phase of the procedure. We observed a high acute success rate (99%), a low complication rate (0.4%), and a 6-month recurrence rate of 3.8%. There was a significant improvement in the patient's symptoms and quality of life as measured by patient global assessment. The routine use of a 3D mapping system during right-sided ablation was associated with low radiation exposure and associated with high acute success rate, low complications, and recurrence rate along with significant improvement in quality of life. The data confirm the reproducibility of this approach in real-world settings across different healthcare systems, and operator experience supporting this approach to minimize radiation exposure without compromising efficacy and safety. NCT04716270.

Objective performance indicators versus GEARS: an opportunity for more accurate assessment of surgical skill.

Surgical skill evaluation that relies on subjective scoring of surgical videos can be time-consuming and inconsistent across raters. We demonstrate differentiated opportunities for objective evaluation to improve surgeon training and performance. Subjective evaluation was performed using the Global evaluative assessment of robotic skills (GEARS) from both expert and crowd raters; whereas, objective evaluation used objective performance indicators (OPIs) derived from da Vinci surgical systems. Classifiers were trained for each evaluation method to distinguish between surgical expertise levels. This study includes one clinical task from a case series of robotic-assisted sleeve gastrectomy procedures performed by a single surgeon, and two training tasks performed by novice and expert surgeons, i.e., surgeons with no experience in robotic-assisted surgery (RAS) and those with more than 500 RAS procedures. When comparing expert and novice skill levels, OPI-based classifier showed significantly higher accuracy than GEARS-based classifier on the more complex dissection task (OPI 0.93 ± 0.08 vs. GEARS 0.67 ± 0.18; 95% CI, 0.16-0.37; p = 0.02), but no significant difference was shown on the simpler suturing task. For the single-surgeon case series, both classifiers performed well when differentiating between early and late group cases with smaller group sizes and larger intervals between groups (OPI 0.9 ± 0.08; GEARS 0.87 ± 0.12; 95% CI, 0.02-0.04; p = 0.67). When increasing the group size to include more cases, thereby having smaller intervals between groups, OPIs demonstrated significantly higher accuracy (OPI 0.97 ± 0.06; GEARS 0.76 ± 0.07; 95% CI, 0.12-0.28; p = 0.004) in differentiating between the early/late cases. Objective methods for skill evaluation in RAS outperform subjective methods when (1) differentiating expertise in a technically challenging training task, and (2) identifying more granular differences along early versus late phases of a surgeon learning curve within a clinical task. Objective methods offer an opportunity for more accessible and scalable skill evaluation in RAS.

Feasibility of a Novel Movement Preference Approach to Classify Case Complexity for Adults with Non-Specific Chronic Low Back Pain

The non-specific nature of low back pain (LBP) poses challenges in its diagnosis and clinical management. Classifying case complexity with an exercise method may help overcome these challenges. The present study proposed a movement-based classification system based on Dance Medicine Australia (DMA) Clinical Pilates for patients with non-specific chronic LBP. To test the feasibility of the proposed system, 40 adults with non-specific chronic LBP were assessed on their movement preference (i.e., movement directions that can relieve pain or are pain-free) through the DMA Clinical Pilates method. The movement preferences could be a combination of each of the following movement directions: (1) flexion or extension, (2) left or right lateral flexion and/or (3) left or right rotation. For cases that had central or bilateral pain, the number of movement preferences identified was used to guide the classification. Using the proposed system, all 40 (100%) LBP cases were successfully classified into basic (n = 8, 20%), intermediate (n = 17, 42.5%), advanced (n = 8, 20%) or expert (n = 7, 17.5%) levels of complexity. In conclusion, this study has demonstrated that the proposed movement-based classification system was a feasible method for classifying case complexity in adults with non-specific chronic LBP. Future clinical intervention studies are needed to confirm if this classification system can enhance therapeutic outcomes in patients.