{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:19:12Z","timestamp":1780391952574,"version":"3.54.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100015098","name":"Seoul Metropolitan Government\u2013Seoul National University Boramae Medical Center","doi-asserted-by":"crossref","award":["04-2024-0033"],"award-info":[{"award-number":["04-2024-0033"]}],"id":[{"id":"10.13039\/501100015098","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Research Foundation of KORE","award":["RS-2023-00241887"],"award-info":[{"award-number":["RS-2023-00241887"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-46000-3","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T15:35:51Z","timestamp":1775835351000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a machine learning model using structured and unstructured features for predicting surgery among patients with carpal tunnel syndrome: development and validation"],"prefix":"10.1038","volume":"16","author":[{"given":"Ji Hyun","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borim","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"46000_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2474-15-399","volume":"15","author":"Y-W Chang","year":"2014","unstructured":"Chang, Y.-W. et al. Comparative effectiveness of ultrasound and paraffin therapy in patients with carpal tunnel syndrome: A randomized trial. BMC Musculoskelet. Disord. 15, 1\u20137 (2014).","journal-title":"BMC Musculoskelet. Disord."},{"key":"46000_CR2","first-page":"34","volume":"8","author":"B Garc\u00eda","year":"2022","unstructured":"Garc\u00eda, B. et al. Carpal tunnel syndrome, generalities, management and treatment. EPRA Int. J. Multidiscip. Res. 8, 34\u201337 (2022).","journal-title":"EPRA Int. J. Multidiscip. Res."},{"issue":"4","key":"46000_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/msc.70024","volume":"22","author":"T Gebrye","year":"2024","unstructured":"Gebrye, T. et al. Global and regional prevalence of carpal tunnel syndrome: A meta\u2010analysis based on a systematic review. Musculoskelet. Care 22(4), e70024 (2024).","journal-title":"Musculoskelet. Care"},{"issue":"4","key":"46000_CR4","doi-asserted-by":"publisher","DOI":"10.3988\/jcn.2021.17.4.524","volume":"17","author":"SY Rhee","year":"2021","unstructured":"Rhee, S. Y. et al. Incidence and reappraisal of known risk factors associated with carpal tunnel syndrome: A nationwide, 11-year, population-based study in South Korea. J. Clin. Neurol. (Seoul, Korea) 17(4), 524 (2021).","journal-title":"J. Clin. Neurol. (Seoul, Korea)"},{"key":"46000_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00264-025-06497-1","author":"YM Mekki","year":"2025","unstructured":"Mekki, Y. M. et al. Applications of artificial intelligence in ultrasound imaging for carpal-tunnel syndrome diagnosis: A scoping review. Int. Orthop. https:\/\/doi.org\/10.1007\/s00264-025-06497-1 (2025).","journal-title":"Int. Orthop."},{"issue":"04","key":"46000_CR6","doi-asserted-by":"publisher","DOI":"10.4236\/ojtr.2019.74012","volume":"7","author":"I Anwar","year":"2019","unstructured":"Anwar, I. et al. Hand function among patients with carpal tunnel syndrome. Open J. Ther. Rehabil. 7(04), 170 (2019).","journal-title":"Open J. Ther. Rehabil."},{"issue":"3","key":"46000_CR7","doi-asserted-by":"publisher","first-page":"226","DOI":"10.15417\/issn.1852-7434.2024.89.3.1822","volume":"89","author":"MS Ferraguti","year":"2024","unstructured":"Ferraguti, M. S. et al. Effectiveness of the six-item carpal tunnel symptoms scale (CTS-6) questionnaire for the diagnosis of carpal tunnel syndrome. J. Asoc. Argent. Ortop. Traumatol. 89(3), 226\u2013232 (2024).","journal-title":"J. Asoc. Argent. Ortop. Traumatol."},{"issue":"12","key":"46000_CR8","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1016\/S1474-4422(16)30231-9","volume":"15","author":"L Padua","year":"2016","unstructured":"Padua, L. et al. Carpal tunnel syndrome: Clinical features, diagnosis, and management. Lancet Neurol. 15(12), 1273\u20131284 (2016).","journal-title":"Lancet Neurol."},{"key":"46000_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.cnp.2018.02.005","volume":"3","author":"M Sonoo","year":"2018","unstructured":"Sonoo, M. et al. Nerve conduction studies and EMG in carpal tunnel syndrome: Do they add value?. Clin. Neurophysiol. Pract. 3, 78\u201388 (2018).","journal-title":"Clin. Neurophysiol. Pract."},{"key":"46000_CR10","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s11999-010-1637-5","volume":"469","author":"JR Fowler","year":"2011","unstructured":"Fowler, J. R., Gaughan, J. P. & Ilyas, A. M. The sensitivity and specificity of ultrasound for the diagnosis of carpal tunnel syndrome: A meta-analysis. Clin. Orthop. Relat. Res. 469, 1089\u20131094 (2011).","journal-title":"Clin. Orthop. Relat. Res."},{"issue":"1","key":"46000_CR11","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1227\/neu.0000000000002848","volume":"95","author":"NL Loos","year":"2024","unstructured":"Loos, N. L. et al. Algorithm versus expert: Machine learning versus surgeon-predicted symptom improvement after carpal tunnel release. Neurosurgery 95(1), 110\u2013117 (2024).","journal-title":"Neurosurgery"},{"key":"46000_CR12","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2024.2417200","author":"E \u00d6ten","year":"2024","unstructured":"\u00d6ten, E., Bilecik, N. A. & U\u011fur, L. Use of machine learning methods in diagnosis of carpal tunnel syndrome. Comput. Methods Biomech. Biomed. Eng. https:\/\/doi.org\/10.1080\/10255842.2024.2417200 (2024).","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"issue":"5","key":"46000_CR13","doi-asserted-by":"publisher","first-page":"216","DOI":"10.32996\/jcsts.2024.6.5.18","volume":"6","author":"MR Hossain","year":"2024","unstructured":"Hossain, M. R. et al. Natural language processing (NLP) in analyzing electronic health records for better decision making. J. Comput. Sci. Technol. Stud. 6(5), 216\u2013228 (2024).","journal-title":"J. Comput. Sci. Technol. Stud."},{"key":"46000_CR14","doi-asserted-by":"publisher","DOI":"10.2196\/43014","volume":"7","author":"E Sezgin","year":"2023","unstructured":"Sezgin, E. et al. Extracting medical information from free-text and unstructured patient-generated health data using natural language processing methods: Feasibility study with real-world data. JMIR Formative Res. 7, e43014 (2023).","journal-title":"JMIR Formative Res."},{"key":"46000_CR15","doi-asserted-by":"crossref","unstructured":"Ye, J., et al. Development and application of natural language processing on unstructured data in hypertension: A scoping review. medRxi 2024\u201302 (2024).","DOI":"10.1101\/2024.02.27.24303468"},{"key":"46000_CR16","unstructured":"Kayi, E. S., et al. Topic modeling for classification of clinical reports. arXiv preprint https:\/\/arxiv.org\/abs\/1706.06177 (2017)."},{"key":"46000_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-0973-y","volume":"19","author":"S Geletta","year":"2019","unstructured":"Geletta, S., Follett, L. & Laugerman, M. Latent Dirichlet allocation in predicting clinical trial terminations. BMC Med. Inform. Decis. Mak. 19, 1\u201312 (2019).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"46000_CR18","doi-asserted-by":"crossref","unstructured":"Danler, M., et al. Visualizing nursing narratives: An Evaluation of latent Dirichlet allocation topic modeling for care reports. In Digital Health and Informatics Innovations for Sustainable Health Care Systems, 1709\u20131713 (IOS Press, 2024).","DOI":"10.3233\/SHTI240752"},{"key":"46000_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2024.104850","volume":"158","author":"S Gan","year":"2024","unstructured":"Gan, S. et al. Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study. Int. J. Nurs. Stud. 158, 104850 (2024).","journal-title":"Int. J. Nurs. Stud."},{"key":"46000_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2024.104797","volume":"156","author":"Y-Z Huang","year":"2024","unstructured":"Huang, Y.-Z. et al. A nursing note-aware deep neural network for predicting mortality risk after hospital discharge. Int. J. Nurs. Stud. 156, 104797 (2024).","journal-title":"Int. J. Nurs. Stud."},{"issue":"18","key":"46000_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13185503","volume":"13","author":"C-C Chiu","year":"2024","unstructured":"Chiu, C.-C. et al. Predicting ICU readmission from electronic health records via BERTopic with long short term memory network approach. J. Clin. Med. 13(18), 5503 (2024).","journal-title":"J. Clin. Med."},{"issue":"4","key":"46000_CR22","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1093\/mr\/road076","volume":"34","author":"B Aalaie","year":"2024","unstructured":"Aalaie, B. et al. Development and validation of a clinical model for predicting the severity of carpal tunnel syndrome. Mod. Rheumatol. 34(4), 851\u2013857 (2024).","journal-title":"Mod. Rheumatol."},{"issue":"12","key":"46000_CR23","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1016\/j.jhsa.2018.05.017","volume":"43","author":"MC Jansen","year":"2018","unstructured":"Jansen, M. C. et al. Predicting clinical outcome after surgical treatment in patients with carpal tunnel syndrome. J. Hand Surg. 43(12), 1098\u20131106 (2018).","journal-title":"J. Hand Surg."},{"issue":"2","key":"46000_CR24","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1053\/jhsu.2003.50031","volume":"28","author":"SE Edgell","year":"2003","unstructured":"Edgell, S. E. et al. Predicting the outcome of carpal tunnel release. J. Hand Surg. 28(2), 255\u2013261 (2003).","journal-title":"J. Hand Surg."},{"key":"46000_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmecsci.2025.110519","author":"D Xu","year":"2025","unstructured":"Xu, D. et al. Data-driven deep learning for predicting ligament fatigue failure risk mechanisms. Int. J. Mech. Sci. https:\/\/doi.org\/10.1016\/j.ijmecsci.2025.110519 (2025).","journal-title":"Int. J. Mech. Sci."},{"key":"46000_CR26","doi-asserted-by":"publisher","DOI":"10.34133\/bmef.0048","volume":"5","author":"SY Selcuk","year":"2024","unstructured":"Selcuk, S. Y. et al. Automated HER2 scoring in breast cancer images using deep learning and pyramid sampling. BME Front. 5, 0048 (2024).","journal-title":"BME Front."},{"key":"46000_CR27","doi-asserted-by":"publisher","DOI":"10.1136\/tsaco-2023-001222","author":"J Luu","year":"2024","unstructured":"Luu, J. et al. Practical guide to building machine learning-based clinical prediction models using imbalanced datasets. Trauma Surg. Acute Care Open https:\/\/doi.org\/10.1136\/tsaco-2023-001222 (2024).","journal-title":"Trauma Surg. Acute Care Open"},{"key":"46000_CR28","unstructured":"OHDSI-observational health data sciences and informatics. https:\/\/www.ohdsi.org\/ (2020)."},{"key":"46000_CR29","doi-asserted-by":"crossref","unstructured":"Hripcsak, G., et al. Observational health data sciences and informatics (OHDSI): Opportunities for observational researchers. In MEDINFO 2015: eHealth-Enabled Health, 574\u2013578 (IOS Press, 2015).","DOI":"10.3233\/978-1-61499-564-7-574"},{"key":"46000_CR30","doi-asserted-by":"crossref","unstructured":"Schuemie, M., et al. FeatureExtraction: Generating Features for a Cohort (2024).","DOI":"10.32614\/CRAN.package.FeatureExtraction"},{"issue":"8","key":"46000_CR31","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1093\/jamia\/ocy032","volume":"25","author":"JM Reps","year":"2018","unstructured":"Reps, J. M. et al. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J. Am. Med. Inform. Assoc. 25(8), 969\u2013975 (2018).","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"8","key":"46000_CR32","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1002\/1097-4598(200008)23:8<1280::AID-MUS20>3.0.CO;2-Y","volume":"23","author":"JDP Bland","year":"2000","unstructured":"Bland, J. D. P. A neurophysiological grading scale for carpal tunnel syndrome. Muscle Nerve Off. J. Am. Assoc. Electrodiagn. Med. 23(8), 1280\u20131283 (2000).","journal-title":"Muscle Nerve Off. J. Am. Assoc. Electrodiagn. Med."},{"issue":"Jan","key":"46000_CR33","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993\u20131022 (2003).","journal-title":"J. Mach. Learn. Res."},{"key":"46000_CR34","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar, H. et al. Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey. Multimed. Tools Appl. 78, 15169\u201315211 (2019).","journal-title":"Multimed. Tools Appl."},{"issue":"3","key":"46000_CR35","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1093\/jamia\/ocw136","volume":"24","author":"JH Chen","year":"2017","unstructured":"Chen, J. H. et al. Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets. J. Am. Med. Inform. Assoc. 24(3), 472\u2013480 (2017).","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"46000_CR36","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","volume":"70","author":"L Meier","year":"2008","unstructured":"Meier, L., Van De Geer, S. & B\u00fchlmann, P. The group lasso for logistic regression. J. R. Stat. Soc. B Stat. Methodol. 70(1), 53\u201371 (2008).","journal-title":"J. R. Stat. Soc. B Stat. Methodol."},{"key":"46000_CR37","doi-asserted-by":"crossref","unstructured":"Dhanka, S &, Maini, S. Multiple machine learning intelligent approaches for the heart disease diagnosis. In IEEE EUROCON 2023\u201420th International Conference on Smart Technologies (IEEE, 2023).","DOI":"10.1109\/EUROCON56442.2023.10199080"},{"key":"46000_CR38","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"issue":"1","key":"46000_CR39","doi-asserted-by":"publisher","DOI":"10.2196\/41030","volume":"1","author":"C Lee","year":"2022","unstructured":"Lee, C. et al. Chronic disease prediction using the common data model: Development study. JMIR AI 1(1), e41030 (2022).","journal-title":"JMIR AI"},{"key":"46000_CR40","doi-asserted-by":"crossref","unstructured":"B\u00fchlmann, P. & Hothorn, T. Boosting Algorithms: Regularization, Prediction and Model Fitting, 477\u2013505 (2007).","DOI":"10.1214\/07-STS242"},{"key":"46000_CR41","doi-asserted-by":"publisher","DOI":"10.2307\/2531595","author":"ER DeLong","year":"1988","unstructured":"DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics https:\/\/doi.org\/10.2307\/2531595 (1988).","journal-title":"Biometrics"},{"issue":"1","key":"46000_CR42","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","volume":"3","author":"WJ Youden","year":"1950","unstructured":"Youden, W. J. Index for rating diagnostic tests. Cancer 3(1), 32\u201335 (1950).","journal-title":"Cancer"},{"issue":"Suppl 13","key":"46000_CR43","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-16-S13-S8","volume":"16","author":"W Zhao","year":"2015","unstructured":"Zhao, W. et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinform. 16(Suppl 13), S8 (2015).","journal-title":"BMC Bioinform."},{"issue":"10","key":"46000_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/e23101301","volume":"23","author":"J Gan","year":"2021","unstructured":"Gan, J. & Qi, Y. Selection of the optimal number of topics for LDA topic model\u2014Taking patent policy analysis as an example. Entropy 23(10), 1301 (2021).","journal-title":"Entropy"},{"issue":"01","key":"46000_CR45","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1055\/s-0038-1668552","volume":"14","author":"M Masud","year":"2019","unstructured":"Masud, M. et al. Does the duration and severity of symptoms have an impact on relief of symptoms after carpal tunnel release?. J. Brachial Plexus Peripher. Nerve Inj. 14(01), e1\u2013e8 (2019).","journal-title":"J. Brachial Plexus Peripher. Nerve Inj."},{"issue":"1","key":"46000_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97043-7","volume":"11","author":"D Park","year":"2021","unstructured":"Park, D. et al. Machine learning-based approach for disease severity classification of carpal tunnel syndrome. Sci. Rep. 11(1), 17464 (2021).","journal-title":"Sci. Rep."},{"issue":"4","key":"46000_CR47","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0300044","volume":"19","author":"M Yeti\u015f","year":"2024","unstructured":"Yeti\u015f, M. et al. Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement. PLoS ONE 19(4), e0300044 (2024).","journal-title":"PLoS ONE"},{"issue":"4","key":"46000_CR48","doi-asserted-by":"publisher","DOI":"10.1097\/GOX.0000000000004279","volume":"10","author":"CJ Harrison","year":"2022","unstructured":"Harrison, C. J. et al. Developing machine learning algorithms to support patient-centered, value-based carpal tunnel decompression surgery. Plast. Reconstruct. Surg. Glob. Open 10(4), e4279 (2022).","journal-title":"Plast. Reconstruct. Surg. Glob. Open"},{"issue":"3","key":"46000_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13030492","volume":"13","author":"M Elseddik","year":"2023","unstructured":"Elseddik, M. et al. Predicting CTS diagnosis and prognosis based on machine learning techniques. Diagnostics 13(3), 492 (2023).","journal-title":"Diagnostics"},{"issue":"4","key":"46000_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/app15041812","volume":"15","author":"A Inui","year":"2025","unstructured":"Inui, A. et al. Prediction of electrophysiological severity and carpal tunnel syndrome instrument changes after carpal tunnel release using machine learning model. Appl. Sci. 15(4), 1812 (2025).","journal-title":"Appl. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46000-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46000-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46000-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T08:51:17Z","timestamp":1780390277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46000-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["46000"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-46000-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"assertion":[{"value":"31 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The study protocol was approved by the Institutional Review Board of the study site (IRB No. 40-2024-17), and the study adhered to the principles of the Declaration of Helsinki. The requirements for informed consent from the study participants were waived by the IRB Committee of the SMG-SNU BMC owing to the retrospective study design.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"16934"}}