Robotic ATLAS: Adapting Advanced Laparoscopic Suturing Training to a Robotic Platform with Proficiency Benchmark Scores

Robotic ATLAS: Adapting Advanced Laparoscopic Suturing Training to a Robotic Platform with Proficiency Benchmark Scores | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robotic ATLAS: Adapting Advanced Laparoscopic Suturing Training to a Robotic Platform with Proficiency Benchmark Scores Nicholas Jonas, Amber Chen-Goodspeed, Shaher Yousef, Chiu-Hsieh Hsu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7681931/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background : The Advanced Training in Laparoscopic Suturing (ATLAS) curriculum teaches complex suturing skills. With the rise of robotic-assisted surgery, it was adapted for robotic platforms (R-ATLAS). Objectives : To adapt ATLAS for robotic suturing and establish expert-derived proficiency benchmarks. Methods : Six ATLAS tasks were modified for robotic instrumentation, and a new non-dominant hand suturing task (3ND) was added. Four expert robotic surgeons each performed five repetitions per task using a robotic trainer. Performances were video-recorded, scored by an independent reviewer, and outliers (±2 SD) excluded. Trimmed means and adjusted SDs defined Basic, Advanced, and Expert benchmarks. NASA Task Load Index (NASA-TLX) assessed workload. Results : Six of 120 attempts were excluded. Benchmarks were established for all seven tasks. NASA-TLX scores ranged from 14.5–30, with Task 3 and 3ND most demanding. Conclusions : R-ATLAS offers a structured, proficiency-based robotic suturing curriculum for integration into surgical training. Education robotics laparoscopy training Highlights • R-ATLAS adapts the ATLAS curriculum for robotic suturing training. • Proficiency benchmarks were defined using expert robotic surgeon performance. • Task 3 and 3ND had the highest NASA-TLX workload; Task 4 had the lowest. • R-ATLAS offers a structured, reproducible framework for robotic skill development. Introduction The use of robotic-assisted surgery has steadily increased over the past decade. A 2020 study published in JAMA by Sheetz et al. highlighted this trend, reporting a rise in robotic utilization for procedures such as inguinal hernia repairs from 0.7% in 2012 to 28.8% in 2018 ( 1 ) . This growth is even more pronounced within minimally invasive surgical (MIS) fellowship programs. In 2021, robotic platforms were used in 23.2% of MIS cases, up from just 2% in 2010, and more than 90% of MIS fellowship programs included robotic procedures in their training ( 2 ) . Similarly, a 2019 national survey of general surgery residency programs found that 92% of programs involved residents in robotic surgery, with 68% offering a formal robotic surgery curriculum ( 3 ) . The expanding role of robotics in surgery is supported by a growing body of literature pointing to benefits such as improved dexterity and visualization, which have been shown to enhance patient care ( 4 – 6 ) . As robotic surgery becomes more widespread, there is a corresponding need for robust training programs tailored to both surgical residents and practicing surgeons. Like any technical skill, robotic surgery requires structured training to develop proficiency. The Simulation-based Mastery Learning model (SBML)—widely applied in courses such as Advanced Cardiac Life Support (ACLS)—advocates for high-fidelity, low-risk environments to build competence in high-stakes procedures ( 7 ) . SBML has been shown in randomized controlled trials to improve patient outcomes ( 8 ) . Evidence from laparoscopic training programs, including the Fundamentals of Laparoscopic Surgery (FLS) and Advanced Training in Laparoscopic Suturing (ATLAS), demonstrates that increased simulation practice—particularly when extended to the point of performance consistency—leads to measurable gains in operative efficiency ( 9 – 11 ) . Importantly, improvements in simulation settings are linked to enhanced surgical outcomes in the operating room ( 9 , 12 , 13 ) . Virtual reality (VR) simulators for the da Vinci Surgical System are available and have shown value in improving skills using inanimate models ( 14 ) . However, current evidence suggests that skill transfer to the operating room remains limited ( 15 ) . While most robotic training programs incorporate the da Vinci Skills Simulator (dVSS), these VR platforms are primarily effective in teaching basic console usage and system familiarity rather than operative skill acquisition ( 16 ) . As a result, many curricula also incorporate non-VR modalities, such as dry and wet labs using the da Vinci console and patient cart, to provide more comprehensive training ( 17 ) . In 2013, a Fellowship Council survey identified a gap in advanced laparoscopic training among graduating residents, prompting the development of the ATLAS curriculum ( 18 , 19 ) . Officially launched by the Association of Surgical Education (ASE) in 2022, ATLAS has demonstrated improvements in resident performance, and proficiency benchmarks have recently been defined ( 12 , 20 ) . In light of the increasing emphasis on robotic skills in surgical training and the success of ATLAS in enhancing laparoscopic proficiency, this study introduces the Robotic Advanced Training in Laparoscopic Suturing (R-ATLAS) program. R-ATLAS builds upon the ATLAS curriculum by adapting its principles to robotic platforms, allowing trainees to develop advanced suturing skills using a physical simulator. The overarching objective is to support surgeons in achieving higher levels of proficiency in robotic suturing. Methods The Advanced Training in Laparoscopic Suturing (ATLAS) curriculum comprises six progressive tasks designed to cultivate advanced laparoscopic suturing skills ( 21 ) . These tasks include: Needle Handling (Task 1), Offset Camera Forehand Suture (Task 2), Offset Camera Backhand Suture (Task 3), Confined Space Suturing (Task 4), Tension Suturing (Task 5), and Running Suturing (Task 6). To adapt ATLAS for robotic surgery, the tasks were transitioned from a laparoscopic box trainer to the Intuitive Abdominal Dome Trainer. Robotic port placement and instrument arm configurations were selected to closely replicate those of the original laparoscopic tasks. A 0-degree camera was utilized and kept stationary throughout all tasks to maintain consistency with the original ATLAS framework, facilitating future comparative studies. Suture materials and lengths remained identical to those used in the laparoscopic version. An R-ATLAS guidebook was developed to support curriculum implementation (Supplemental Material). It included detailed instructions on task setup, descriptions, performance time limits, and error metrics. Additionally, a novel task—Task 3 Non-Dominant (3ND)—was introduced to evaluate the feasibility of using the non-dominant hand for forehand suturing on a robotic platform. This addition was based on the hypothesis that robotic surgeons may prefer using their non-dominant hand over performing backhand sutures in certain operative scenarios. Study Design All participants were provided with the standard ATLAS task kit and access to the Intuitive Abdominal Dome Trainer. Following a brief familiarization period—during which each surgeon practiced Tasks 1 through 6, including Task 3ND—participants completed five consecutive repetitions of each task. Each attempt was recorded using a smartphone positioned within the Dome Trainer. Performance was evaluated via video review by an independent, trained rater ( 22 ) . Upon completing each task, participants assessed workload using the NASA Task Load Index (NASA-TLX), a validated tool that captures subjective workload across six domains: mental demand, physical demand, temporal demand, performance, effort, and frustration. Raw NASA-TLX scores were calculated by summing each domain score (ranging from 1 to 21 per domain, for a total range of 6 to 126). The median and interquartile range (IQR) were calculated for each scale and total score. Performance Metrics and Proficiency Standard Setting Proficiency benchmarks were determined using a standard-setting methodology previously established in surgical education research ( 20 , 23 ) . For each task, the mean and standard deviation (SD) were calculated based on all five repetitions per participant. Outliers—defined as values more than two SDs from the mean for either time or error—were removed to calculate trimmed means. Three proficiency thresholds were established: Basic (-2 SD), Advanced (-1 SD), and Expert (trimmed mean of expert group). All final proficiency cutoffs were rounded to the nearest whole number. A non-compensatory approach was used, requiring learners to meet or exceed the proficiency threshold for both time and error on each task individually in order to be considered proficient. Results Four experts in robotic suturing were selected for this study based on their extensive experience with robotic suturing in clinical practice. These individuals represented four different institutions (Table 1 ). The independent rater had significant experience in grading ATLAS tasks. Table 1 Surgeon Demographics Surgeon Years in Practice Years Performing Robotic Surgery 1 8 5 2 2 2 3 10 3 4 3 2.5 Expert-Derived Proficiency Levels Errors were noted for the following repetitions: Task 1 had one bent needle; Task 2 had one non-full thickness bite; Task 4 had one non-full thickness bite. Other tasks had no reported errors. Prociency scores are summarized in Table 2 . Table 2 Expert-Derived Proficiency Scores for the R-ATLAS Curriculum Task Basic Proficiency (points) Advanced Proficiency (points) Expert Proficiency (points) 1 – Needle Handling 131 175 218 2 – Offset Camera Forehand Suturing 463 489 515 3 – Offset Camera Backhand Suturing 491 507 522 4 – Confined Space Suturing 469 490 511 5 – Tension Suturing 173 189 205 6 – Running Suturing 665 684 703 3ND – Non-dominant Forehand Suturing 489 508 526 NASA-TLX Scores NASA-TLX scores are summarized in Table 3 . Table 3 Raw NASA-TLX Scores by Task Task Mental Demand Physical Demand Temporal Demand Performance Effort Frustration Total Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3) 1 3 (3, 3.25) 3.5 (3,4.75) 3.5 (3, 4.5) 4.5 (3.75, 6.5) 3.5 (3, 5.75) 4 (2.75, 5.5) 23 (19.75, 30) 2 4 (2.5, 6) 4 (3.25, 6.5) 3 (2.5, 4.5) 3.5 (2, 7) 3.5 (2.5, 6.25) 3.5 (2.5, 6.5) 21.5 (16.75, 35.25) 3 2.5 (2,6) 3 (2, 7) 2.5 (2, 5) 3.5 (2, 7.75) 3 (2, 7.25) 3 (2, 7.25) 17.5 (12, 40.25) 4 2.5 (1.75, 4.5) 2.5 (2, 5.25) 2.5 (1.75, 3.75) 3 (1.75, 4.75) 2.5 (1.75, 5.5) 2.5 (1.75, 3.75) 15.5 (10.75, 27.5) 5 3 (2.75, 3.5) 3.5 (2.75, 4.25) 3 (2.75, 3) 3.5 (2.75, 4.25) 3.5 (2.75, 4.25) 3 (2.75, 3.25) 19.5 (16.5, 22.5) 6 3 (2.75, 4) 2.5 (2, 3) 3 (3, 6.25) 3.5 (3, 4) 3 (3, 4.5) 3 (2.75, 3.25) 18 (16.75, 24.75) 3ND 3.5 (2.5, 5) 3 (2, 8.5) 3 (2, 4.5) 5.5 (3, 9) 6 (2.5, 10) 3.5 (2, 9) 30 (14, 46) Discussion This study aimed to develop a robotic-based curriculum for advanced suturing and to establish expert-derived proficiency benchmarks. R-ATLAS was successfully implemented using commercially available resources, offering hands-on training on a physical model. Unlike many programs relying exclusively on VR platforms, R-ATLAS supports tactile practice and skill acquisition. The expert-derived benchmarks reflect realistic performance targets for experienced robotic surgeons. These thresholds may inform goal-setting across training levels. Low NASA-TLX scores across tasks suggest robotic platforms may ease complex maneuvers. The curriculum’s strength lies in its adaptability from ATLAS and its reproducibility, aided by the guidebook. It enables direct comparison with laparoscopic performance while highlighting opportunities to create robotic-optimized tasks, such as Task 3ND. Future studies should validate R-ATLAS in trainee populations and evaluate its impact on clinical outcomes. Limitations include a small expert sample and lack of trainee data. Use of laparoscopic task structure may underutilize robotic capabilities, such as camera angulation. Further development should focus on robotic-specific task design and direct comparison of robotic vs. laparoscopic skill acquisition. Conclusions The need for structured training in advanced robotic suturing is evident. R-ATLAS provides an expert-derived framework for skill development, offering proficiency thresholds applicable across training levels. Early data suggest it may be easier to learn than its laparoscopic counterpart, warranting further validation in broader learner populations. Declarations Disclosures The authors report no disclosures. References Sheetz KH, Claflin J, Dimick JB. Trends in the Adoption of Robotic Surgery for Common Surgical Procedures. JAMA Netw Open. 2020;3(1). https://doi.org/10.1001/jamanetworkopen.2019.18911 . Haywood N, Scott J, Zhang A, Hallowell P, Schirmer B. 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Supplementary Files RATLASGuidebook.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Nov, 2025 Reviewers invited by journal 16 Nov, 2025 Editor invited by journal 14 Nov, 2025 Editor assigned by journal 14 Nov, 2025 First submitted to journal 10 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7681931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545922547,"identity":"434a8f7e-70ee-493a-a43b-5d95c61f4ce1","order_by":0,"name":"Nicholas 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13:09:10","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":97308133,"visible":true,"origin":"","legend":"","description":"","filename":"RATLASGuidebook.docx","url":"https://assets-eu.researchsquare.com/files/rs-7681931/v1/36b4e0936afb7e4b639ed279.docx"}],"financialInterests":"","formattedTitle":"Robotic ATLAS: Adapting Advanced Laparoscopic Suturing Training to a Robotic Platform with Proficiency Benchmark Scores","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; R-ATLAS adapts the ATLAS curriculum for robotic suturing training.\u003c/p\u003e\u003cp\u003e\u0026bull; Proficiency benchmarks were defined using expert robotic surgeon performance.\u003c/p\u003e\u003cp\u003e\u0026bull; Task 3 and 3ND had the highest NASA-TLX workload; Task 4 had the lowest.\u003c/p\u003e\u003cp\u003e\u0026bull; R-ATLAS offers a structured, reproducible framework for robotic skill development.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe use of robotic-assisted surgery has steadily increased over the past decade. A 2020 study published in JAMA by Sheetz et al. highlighted this trend, reporting a rise in robotic utilization for procedures such as inguinal hernia repairs from 0.7% in 2012 to 28.8% in 2018 \u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. This growth is even more pronounced within minimally invasive surgical (MIS) fellowship programs. In 2021, robotic platforms were used in 23.2% of MIS cases, up from just 2% in 2010, and more than 90% of MIS fellowship programs included robotic procedures in their training \u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e. Similarly, a 2019 national survey of general surgery residency programs found that 92% of programs involved residents in robotic surgery, with 68% offering a formal robotic surgery curriculum \u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e. The expanding role of robotics in surgery is supported by a growing body of literature pointing to benefits such as improved dexterity and visualization, which have been shown to enhance patient care \u003csup\u003e(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs robotic surgery becomes more widespread, there is a corresponding need for robust training programs tailored to both surgical residents and practicing surgeons. Like any technical skill, robotic surgery requires structured training to develop proficiency. The Simulation-based Mastery Learning model (SBML)\u0026mdash;widely applied in courses such as Advanced Cardiac Life Support (ACLS)\u0026mdash;advocates for high-fidelity, low-risk environments to build competence in high-stakes procedures \u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e. SBML has been shown in randomized controlled trials to improve patient outcomes \u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e. Evidence from laparoscopic training programs, including the Fundamentals of Laparoscopic Surgery (FLS) and Advanced Training in Laparoscopic Suturing (ATLAS), demonstrates that increased simulation practice\u0026mdash;particularly when extended to the point of performance consistency\u0026mdash;leads to measurable gains in operative efficiency \u003csup\u003e(\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e. Importantly, improvements in simulation settings are linked to enhanced surgical outcomes in the operating room \u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eVirtual reality (VR) simulators for the da Vinci Surgical System are available and have shown value in improving skills using inanimate models \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e. However, current evidence suggests that skill transfer to the operating room remains limited \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. While most robotic training programs incorporate the da Vinci Skills Simulator (dVSS), these VR platforms are primarily effective in teaching basic console usage and system familiarity rather than operative skill acquisition \u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e. As a result, many curricula also incorporate non-VR modalities, such as dry and wet labs using the da Vinci console and patient cart, to provide more comprehensive training \u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn 2013, a Fellowship Council survey identified a gap in advanced laparoscopic training among graduating residents, prompting the development of the ATLAS curriculum \u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e. Officially launched by the Association of Surgical Education (ASE) in 2022, ATLAS has demonstrated improvements in resident performance, and proficiency benchmarks have recently been defined \u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. In light of the increasing emphasis on robotic skills in surgical training and the success of ATLAS in enhancing laparoscopic proficiency, this study introduces the Robotic Advanced Training in Laparoscopic Suturing (R-ATLAS) program. R-ATLAS builds upon the ATLAS curriculum by adapting its principles to robotic platforms, allowing trainees to develop advanced suturing skills using a physical simulator. The overarching objective is to support surgeons in achieving higher levels of proficiency in robotic suturing.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe Advanced Training in Laparoscopic Suturing (ATLAS) curriculum comprises six progressive tasks designed to cultivate advanced laparoscopic suturing skills \u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e. These tasks include: Needle Handling (Task 1), Offset Camera Forehand Suture (Task 2), Offset Camera Backhand Suture (Task 3), Confined Space Suturing (Task 4), Tension Suturing (Task 5), and Running Suturing (Task 6).\u003c/p\u003e\u003cp\u003eTo adapt ATLAS for robotic surgery, the tasks were transitioned from a laparoscopic box trainer to the Intuitive Abdominal Dome Trainer. Robotic port placement and instrument arm configurations were selected to closely replicate those of the original laparoscopic tasks. A 0-degree camera was utilized and kept stationary throughout all tasks to maintain consistency with the original ATLAS framework, facilitating future comparative studies. Suture materials and lengths remained identical to those used in the laparoscopic version.\u003c/p\u003e\u003cp\u003eAn R-ATLAS guidebook was developed to support curriculum implementation (Supplemental Material). It included detailed instructions on task setup, descriptions, performance time limits, and error metrics. Additionally, a novel task\u0026mdash;Task 3 Non-Dominant (3ND)\u0026mdash;was introduced to evaluate the feasibility of using the non-dominant hand for forehand suturing on a robotic platform. This addition was based on the hypothesis that robotic surgeons may prefer using their non-dominant hand over performing backhand sutures in certain operative scenarios.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eAll participants were provided with the standard ATLAS task kit and access to the Intuitive Abdominal Dome Trainer. Following a brief familiarization period\u0026mdash;during which each surgeon practiced Tasks 1 through 6, including Task 3ND\u0026mdash;participants completed five consecutive repetitions of each task. Each attempt was recorded using a smartphone positioned within the Dome Trainer.\u003c/p\u003e\u003cp\u003ePerformance was evaluated via video review by an independent, trained rater \u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. Upon completing each task, participants assessed workload using the NASA Task Load Index (NASA-TLX), a validated tool that captures subjective workload across six domains: mental demand, physical demand, temporal demand, performance, effort, and frustration. Raw NASA-TLX scores were calculated by summing each domain score (ranging from 1 to 21 per domain, for a total range of 6 to 126). The median and interquartile range (IQR) were calculated for each scale and total score.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePerformance Metrics and Proficiency Standard Setting\u003c/h3\u003e\n\u003cp\u003eProficiency benchmarks were determined using a standard-setting methodology previously established in surgical education research \u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e. For each task, the mean and standard deviation (SD) were calculated based on all five repetitions per participant. Outliers\u0026mdash;defined as values more than two SDs from the mean for either time or error\u0026mdash;were removed to calculate trimmed means.\u003c/p\u003e\u003cp\u003eThree proficiency thresholds were established: Basic (-2 SD), Advanced (-1 SD), and Expert (trimmed mean of expert group). All final proficiency cutoffs were rounded to the nearest whole number. A non-compensatory approach was used, requiring learners to meet or exceed the proficiency threshold for both time and error on each task individually in order to be considered proficient.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFour experts in robotic suturing were selected for this study based on their extensive experience with robotic suturing in clinical practice. These individuals represented four different institutions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The independent rater had significant experience in grading ATLAS tasks.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSurgeon Demographics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgeon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears in Practice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYears Performing Robotic Surgery\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eExpert-Derived Proficiency Levels\u003c/h3\u003e\n\u003cp\u003eErrors were noted for the following repetitions: Task 1 had one bent needle; Task 2 had one non-full thickness bite; Task 4 had one non-full thickness bite. Other tasks had no reported errors. Prociency scores are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExpert-Derived Proficiency Scores for the R-ATLAS Curriculum\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBasic Proficiency (points)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdvanced Proficiency (points)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpert Proficiency (points)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 \u0026ndash; Needle Handling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 \u0026ndash; Offset Camera Forehand Suturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 \u0026ndash; Offset Camera Backhand Suturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 \u0026ndash; Confined Space Suturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 \u0026ndash; Tension Suturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 \u0026ndash; Running Suturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3ND \u0026ndash; Non-dominant Forehand Suturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eNASA-TLX Scores\u003c/h3\u003e\n\u003cp\u003eNASA-TLX scores are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRaw NASA-TLX Scores by Task\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMental Demand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical Demand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTemporal Demand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePerformance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFrustration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3, 3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5 (3,4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5 (3, 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.5 (3.75, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5 (3, 5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4 (2.75, 5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23 (19.75, 30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.5, 6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (3.25, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2.5, 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (2, 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5 (2.5, 6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.5 (2.5, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.5 (16.75, 35.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5 (2,6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2, 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5 (2, 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (2, 7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (2, 7.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (2, 7.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.5 (12, 40.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5 (1.75, 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5 (2, 5.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5 (1.75, 3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (1.75, 4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.5 (1.75, 5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.5 (1.75, 3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.5 (10.75, 27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2.75, 3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5 (2.75, 4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2.75, 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (2.75, 4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5 (2.75, 4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (2.75, 3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.5 (16.5, 22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2.75, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5 (2, 3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (3, 6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (3, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (3, 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (2.75, 3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (16.75, 24.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5 (2.5, 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2, 8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2, 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.5 (3, 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (2.5, 10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.5 (2, 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30 (14, 46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to develop a robotic-based curriculum for advanced suturing and to establish expert-derived proficiency benchmarks. R-ATLAS was successfully implemented using commercially available resources, offering hands-on training on a physical model. Unlike many programs relying exclusively on VR platforms, R-ATLAS supports tactile practice and skill acquisition.\u003c/p\u003e\u003cp\u003eThe expert-derived benchmarks reflect realistic performance targets for experienced robotic surgeons. These thresholds may inform goal-setting across training levels. Low NASA-TLX scores across tasks suggest robotic platforms may ease complex maneuvers.\u003c/p\u003e\u003cp\u003eThe curriculum\u0026rsquo;s strength lies in its adaptability from ATLAS and its reproducibility, aided by the guidebook. It enables direct comparison with laparoscopic performance while highlighting opportunities to create robotic-optimized tasks, such as Task 3ND. Future studies should validate R-ATLAS in trainee populations and evaluate its impact on clinical outcomes.\u003c/p\u003e\u003cp\u003eLimitations include a small expert sample and lack of trainee data. Use of laparoscopic task structure may underutilize robotic capabilities, such as camera angulation. Further development should focus on robotic-specific task design and direct comparison of robotic vs. laparoscopic skill acquisition.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe need for structured training in advanced robotic suturing is evident. R-ATLAS provides an expert-derived framework for skill development, offering proficiency thresholds applicable across training levels. Early data suggest it may be easier to learn than its laparoscopic counterpart, warranting further validation in broader learner populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no disclosures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSheetz KH, Claflin J, Dimick JB. Trends in the Adoption of Robotic Surgery for Common Surgical Procedures. JAMA Netw Open. 2020;3(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2019.18911\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2019.18911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaywood N, Scott J, Zhang A, Hallowell P, Schirmer B. 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Surg Innov. 2007;14(2):107\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1553350607302329\u003c/span\u003e\u003cspan address=\"10.1177/1553350607302329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"global-surgical-education-journal-of-the-association-for-surgical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"GSED","sideBox":"Learn more about [Global Surgical Education - Journal of the Association for Surgical Education](https://link.springer.com/journal/44186)","snPcode":"44186","submissionUrl":"https://www.editorialmanager.com/gsed/default1.aspx","title":"Global Surgical Education - Journal of the Association for Surgical Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Education, robotics, laparoscopy, training","lastPublishedDoi":"10.21203/rs.3.rs-7681931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7681931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The Advanced Training in Laparoscopic Suturing (ATLAS) curriculum teaches complex suturing skills. With the rise of robotic-assisted surgery, it was adapted for robotic platforms (R-ATLAS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: To adapt ATLAS for robotic suturing and establish expert-derived proficiency benchmarks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Six ATLAS tasks were modified for robotic instrumentation, and a new non-dominant hand suturing task (3ND) was added. Four expert robotic surgeons each performed five repetitions per task using a robotic trainer. Performances were video-recorded, scored by an independent reviewer, and outliers (±2 SD) excluded. Trimmed means and adjusted SDs defined Basic, Advanced, and Expert benchmarks. NASA Task Load Index (NASA-TLX) assessed workload.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Six of 120 attempts were excluded. Benchmarks were established for all seven tasks. NASA-TLX scores ranged from 14.5–30, with Task 3 and 3ND most demanding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: R-ATLAS offers a structured, proficiency-based robotic suturing curriculum for integration into surgical training.\u003c/p\u003e","manuscriptTitle":"Robotic ATLAS: Adapting Advanced Laparoscopic Suturing Training to a Robotic Platform with Proficiency Benchmark Scores","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 13:09:03","doi":"10.21203/rs.3.rs-7681931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-17T22:24:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-17T00:09:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Global Surgical Education - Journal of the Association for Surgical Education","date":"2025-11-14T14:48:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T09:50:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Global Surgical Education - Journal of the Association for Surgical Education","date":"2025-11-10T13:54:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"global-surgical-education-journal-of-the-association-for-surgical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"GSED","sideBox":"Learn more about [Global Surgical Education - Journal of the Association for Surgical Education](https://link.springer.com/journal/44186)","snPcode":"44186","submissionUrl":"https://www.editorialmanager.com/gsed/default1.aspx","title":"Global Surgical Education - Journal of the Association for Surgical Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"374ef8a3-a70a-4e6b-b09b-87212002c6f7","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T04:06:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 13:09:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7681931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7681931","identity":"rs-7681931","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}