Beware of the Plateau Trap: A Multidimensional Analysis Redefining the Learning Curve of Robotic Lobectomy in Non-small Cell Lung Cancer

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Abstract Current definitions of the learning curve for robotic lobectomy are derived from small cohorts and lack standardized criteria for proficiency. Whether these series can accurately distinguish true surgical mastery from improved operative efficiency remains uncertain.We analyzed 300 consecutive patients with non-small cell lung cancer who underwent robotic portal lobectomy with four arms (RPL-4) between June 2018 and July 2023, using a prospectively maintained database. Learning phases were determined using cumulative sum and multivariable analyses. Perioperative outcomes were compared across phases. Three phases were identified: learning (cases 1–81), plateau (82–137), and mastery (>137). Median console time declined significantly across phases (88.0 vs. 69.0 vs. 66.0 minutes, P < 0.001). Median blood loss also decreased (50.0 vs. 50.0 vs. 40.0 mL, P < 0.001). Despite improved efficiency, postoperative complication rates remained elevated during the plateau phase (10.7%), primarily due to Clavien–Dindo Grade II events (8.9%), before decreasing to 3.1% in the mastery phase ( P = 0.016). Furthermore, sustained reductions in chest tube duration and length of stay were observed only after >137 cases. RPL-4 demonstrates a prolonged 3-phase learning curve with a deceptive plateau. During this phase, gains in efficiency obscure persistently high postoperative complication rates, leading to underestimation of the true learning process. Training benchmarks should prioritize comprehensive surgical quality rather than operative speed alone.
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Beware of the Plateau Trap: A Multidimensional Analysis Redefining the Learning Curve of Robotic Lobectomy in Non-small Cell Lung Cancer | 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 Beware of the Plateau Trap: A Multidimensional Analysis Redefining the Learning Curve of Robotic Lobectomy in Non-small Cell Lung Cancer Yi-Qing Lin, Mu-Zi Yang, Jing-Sheng Cai, Zeng-Hao Chang, Hou-Rui Fan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9481537/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Current definitions of the learning curve for robotic lobectomy are derived from small cohorts and lack standardized criteria for proficiency. Whether these series can accurately distinguish true surgical mastery from improved operative efficiency remains uncertain.We analyzed 300 consecutive patients with non-small cell lung cancer who underwent robotic portal lobectomy with four arms (RPL-4) between June 2018 and July 2023, using a prospectively maintained database. Learning phases were determined using cumulative sum and multivariable analyses. Perioperative outcomes were compared across phases. Three phases were identified: learning (cases 1–81), plateau (82–137), and mastery (>137). Median console time declined significantly across phases (88.0 vs. 69.0 vs. 66.0 minutes, P < 0.001). Median blood loss also decreased (50.0 vs. 50.0 vs. 40.0 mL, P < 0.001). Despite improved efficiency, postoperative complication rates remained elevated during the plateau phase (10.7%), primarily due to Clavien–Dindo Grade II events (8.9%), before decreasing to 3.1% in the mastery phase ( P = 0.016). Furthermore, sustained reductions in chest tube duration and length of stay were observed only after >137 cases. RPL-4 demonstrates a prolonged 3-phase learning curve with a deceptive plateau. During this phase, gains in efficiency obscure persistently high postoperative complication rates, leading to underestimation of the true learning process. Training benchmarks should prioritize comprehensive surgical quality rather than operative speed alone. Learning curve Non-small cell lung cancer Robotic Surgical Procedures Lobectomy Postoperative complications Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementBewareofPlateauTrap.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 21 Apr, 2026 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. 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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-9481537","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630308264,"identity":"6779c75c-0115-42d9-b753-64c6fe0f5c32","order_by":0,"name":"Yi-Qing Lin","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Qing","middleName":"","lastName":"Lin","suffix":""},{"id":630308266,"identity":"7c17824d-5dcf-41eb-b992-f383a28e0882","order_by":1,"name":"Mu-Zi Yang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center, 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