{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:57:50Z","timestamp":1778263070346,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004332","name":"Seoul National University Hospital","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004332","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R<jats:sup>2<\/jats:sup>) metrics. Our findings highlighted that Random Forest models excelled in department-specific applications, achieving an MAE of 16.32, an RMSE of 31.19, and an R<jats:sup>2<\/jats:sup> of 0.92, significantly outperforming general models and conventional estimates. This improvement emphasizes the advantage of customizing models to fit the distinct characteristics and data patterns of each department. Additionally, our SHAP-based feature importance analysis identified morning operation timing, ICU ward assignments, operation codes, and surgeon IDs as key factors influencing surgical duration. This suggests that a detailed and nuanced approach to model development can substantially increase prediction accuracy. By providing a more accurate, reliable tool for predicting surgical case durations, our department-specific Random Forest models promise to enhance surgical scheduling, leading to more effective OR management. This approach underscores the importance of leveraging tailored, data-driven models to improve healthcare outcomes and operational efficiency.<\/jats:p>","DOI":"10.1007\/s10916-025-02141-y","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T08:27:29Z","timestamp":1736843249000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach"],"prefix":"10.1007","volume":"49","author":[{"given":"Jung-Bin","family":"Park","sequence":"first","affiliation":[]},{"given":"Gyun-Ho","family":"Roh","sequence":"additional","affiliation":[]},{"given":"Kwangsoo","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hee-Soo","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"issue":"4","key":"2141_CR1","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1213\/ane.0b013e318164f0d5","volume":"106","author":"F Dexter","year":"2008","unstructured":"Dexter, F., Dexter, E. U., Masursky, D., & Nussmeier, N. A. (2008). Systematic review of general thoracic surgery articles to identify predictors of operating room case durations. Anesthesia & Analgesia, 106(4), 1232-1241. https:\/\/doi.org\/10.1213\/ane.0b013e318164f0d5","journal-title":"Anesthesia & Analgesia"},{"issue":"4","key":"2141_CR2","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1097\/00000542-200004000-00035","volume":"92","author":"DP Strum","year":"2000","unstructured":"Strum, D. P., May, J. H., & Vargas, L. G. (2000). Modeling the uncertainty of surgical procedure times: comparison of log-normal and normal models. The Journal of the American Society of Anesthesiologists, 92(4), 1160-1167. https:\/\/doi.org\/10.1097\/00000542-200004000-00035","journal-title":"The Journal of the American Society of Anesthesiologists"},{"issue":"12","key":"2141_CR3","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1001\/archsurg.2010.255","volume":"145","author":"PS Stepaniak","year":"2010","unstructured":"Stepaniak, P. S., Vrijland, W. W., de Quelerij, M., de Vries, G., & Heij, C. (2010). Working with a fixed operating room team on consecutive similar cases and the effect on case duration and turnover time. Archives of surgery, 145(12), 1165-1170. https:\/\/doi.org\/10.1001\/archsurg.2010.255","journal-title":"Archives of surgery"},{"issue":"1","key":"2141_CR4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1213\/ANE.0000000000006015","volume":"135","author":"RA Gabriel","year":"2022","unstructured":"Gabriel, R. A., Harjai, B., Simpson, S., Goldhaber, N., Curran, B. P., & Waterman, R. S. (2022). Machine learning-based models predicting outpatient surgery end time and recovery room discharge at an ambulatory surgery center. Anesthesia & Analgesia, 135(1), 159-169. https:\/\/doi.org\/10.1213\/ANE.0000000000006015","journal-title":"Anesthesia & Analgesia"},{"key":"2141_CR5","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s10729-006-9005-4","volume":"10","author":"B Denton","year":"2007","unstructured":"Denton, B., Viapiano, J., & Vogl, A. (2007). Optimization of surgery sequencing and scheduling decisions under uncertainty. Health care management science, 10, 13-24. https:\/\/doi.org\/10.1007\/s10729-006-9005-4","journal-title":"Health care management science"},{"issue":"5","key":"2141_CR6","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1111\/anae.14958","volume":"75","author":"JJ Pandit","year":"2020","unstructured":"Pandit, J. J. (2020). Rational planning of operating lists: a prospective comparison of \u2018booking to the mean\u2019vs.\u2018probabilistic case scheduling\u2019in urology. Anaesthesia, 75(5), 642-647. https:\/\/doi.org\/10.1111\/anae.14958","journal-title":"Anaesthesia"},{"issue":"9","key":"2141_CR7","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/BF03022054","volume":"52","author":"F Dexter","year":"2005","unstructured":"Dexter, F., Macario, A., Epstein, R. H., & Ledolter, J. (2005). Validity and usefulness of a method to monitor surgical services\u2019 average bias in scheduled case durations. Canadian Journal of Anesthesia\/Journal canadien d'anesth\u00e9sie, 52(9), 935-939. https:\/\/doi.org\/10.1007\/BF03022054","journal-title":"Canadian Journal of Anesthesia\/Journal canadien d'anesth\u00e9sie"},{"issue":"4","key":"2141_CR8","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1213\/ANE.0b013e3181cd6eb9","volume":"110","author":"EU Dexter","year":"2010","unstructured":"Dexter, E. U., Dexter, F., Masursky, D., & Kasprowicz, K. A. (2010). Prospective trial of thoracic and spine surgeons' updating of their estimated case durations at the start of cases. Anesthesia & Analgesia, 110(4), 1164-1168. https:\/\/https:\/\/doi.org\/10.1213\/ANE.0b013e3181cd6eb9","journal-title":"Anesthesia & Analgesia"},{"issue":"1","key":"2141_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/S0925-5273(03)00087-2","volume":"85","author":"A Guinet","year":"2003","unstructured":"Guinet, A., & Chaabane, S. (2003). Operating theatre planning. International Journal of Production Economics, 85(1), 69-81. https:\/\/doi.org\/10.1016\/S0925-5273(03)00087-2","journal-title":"International Journal of Production Economics"},{"issue":"1\u20132","key":"2141_CR10","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ijpe.2004.12.006","volume":"99","author":"A Jebali","year":"2006","unstructured":"Jebali, A., Alouane, A. B. H., & Ladet, P. (2006). Operating rooms scheduling. International Journal of Production Economics, 99(1-2), 52-62. https:\/\/doi.org\/10.1016\/j.ijpe.2004.12.006","journal-title":"International Journal of Production Economics"},{"key":"2141_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1160-5","volume":"43","author":"JP Tuwatananurak","year":"2019","unstructured":"Tuwatananurak, J. P., Zadeh, S., Xu, X., Vacanti, J. A., Fulton, W. R., Ehrenfeld, J. M., & Urman, R. D. (2019). Machine learning can improve estimation of surgical case duration: a pilot study. Journal of medical systems, 43, 1-7. https:\/\/doi.org\/10.1007\/s10916-019-1160-5","journal-title":"Journal of medical systems"},{"issue":"2","key":"2141_CR12","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1097\/HCM.0000000000000214","volume":"37","author":"K Tankard","year":"2018","unstructured":"Tankard, K., Acciavatti, T. D., Vacanti, J. C., Heydarpour, M., Beutler, S. S., Flanagan, H. L., & Urman, R. D. (2018). Contributors to operating room underutilization and implications for hospital administrators. The Health Care Manager, 37(2), 118-128. https:\/\/doi.org\/10.1097\/HCM.0000000000000214","journal-title":"The Health Care Manager"},{"issue":"5","key":"2141_CR13","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1016\/j.bja.2021.12.039","volume":"128","author":"Y Jiao","year":"2022","unstructured":"Jiao, Y., Xue, B., Lu, C., Avidan, M. S., & Kannampallil, T. (2022). Continuous real-time prediction of surgical case duration using a modular artificial neural network. British journal of anaesthesia, 128(5), 829-837. https:\/\/doi.org\/10.1016\/j.bja.2021.12.039","journal-title":"British journal of anaesthesia"},{"issue":"4","key":"2141_CR14","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1001\/jamasurg.2020.6361","volume":"156","author":"CT Str\u00f6mblad","year":"2021","unstructured":"Str\u00f6mblad, C. T., Baxter-King, R. G., Meisami, A., Yee, S. J., Levine, M. R., Ostrovsky, A., ... & Wilson, R. S. (2021). Effect of a predictive model on planned surgical duration accuracy, patient wait time, and use of presurgical resources: a randomized clinical trial. JAMA surgery, 156(4), 315-321. https:\/\/doi.org\/10.1001\/jamasurg.2020.6361","journal-title":"JAMA surgery"},{"issue":"2","key":"2141_CR15","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1177\/01945998221076480","volume":"168","author":"LE Miller","year":"2023","unstructured":"Miller, L. E., Goedicke, W., Crowson, M. G., Rathi, V. K., Naunheim, M. R., & Agarwala, A. V. (2023). Using machine learning to predict operating room case duration: a case study in otolaryngology. Otolaryngology\u2013Head and Neck Surgery, 168(2), 241-247. https:\/\/doi.org\/10.1177\/01945998221076480","journal-title":"Otolaryngology-Head and Neck Surgery"},{"key":"2141_CR16","doi-asserted-by":"publisher","first-page":"e39650","DOI":"10.2196\/39650","volume":"6","author":"RA Gabriel","year":"2023","unstructured":"Gabriel, R. A., Harjai, B., Simpson, S., Du, A. L., Tully, J. L., George, O., & Waterman, R. (2023). An ensemble learning approach to improving prediction of case duration for spine surgery: algorithm development and validation. JMIR Perioperative Medicine, 6, e39650. https:\/\/doi.org\/10.2196\/39650","journal-title":"JMIR Perioperative Medicine"},{"issue":"1","key":"2141_CR17","doi-asserted-by":"publisher","first-page":"e44909","DOI":"10.2196\/44909","volume":"2","author":"S Kendale","year":"2023","unstructured":"Kendale, S., Bishara, A., Burns, M., Solomon, S., Corriere, M., & Mathis, M. (2023). Machine learning for the prediction of procedural case durations developed using a large multicenter database: algorithm development and validation study. JMIR AI, 2(1), e44909. https:\/\/doi.org\/10.2196\/44909","journal-title":"JMIR AI"},{"issue":"22","key":"2141_CR18","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.jclinane.2010.02.003","volume":"4","author":"A Macario","year":"2010","unstructured":"Macario, A. (2010). What does one minute of operating room time cost?. Journal of clinical anesthesia, 4(22), 233-236. https:\/\/doi.org\/10.1016\/j.jclinane.2010.02.003","journal-title":"Journal of clinical anesthesia"},{"key":"2141_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.knosys.2016.06.012","volume":"117","author":"J Maillo","year":"2017","unstructured":"Maillo, J., Ram\u00edrez, S., Triguero, I., & Herrera, F. (2017). kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems, 117, 3-15. https:\/\/doi.org\/10.1016\/j.knosys.2016.06.012","journal-title":"Knowledge-Based Systems"},{"key":"2141_CR20","doi-asserted-by":"publisher","unstructured":"Qi, Y. (2012). Random forest for bioinformatics. Ensemble machine learning: Methods and applications, 307\u2013323. https:\/\/doi.org\/10.1007\/978-1-4419-9326-7_11","DOI":"10.1007\/978-1-4419-9326-7_11"},{"issue":"2","key":"2141_CR21","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1213\/ANE.0000000000005247","volume":"132","author":"P Schober","year":"2021","unstructured":"Schober, P., & Vetter, T. R. (2021). Logistic regression in medical research. Anesthesia & Analgesia, 132(2), 365-366. https:\/\/doi.org\/10.1213\/ANE.0000000000005247","journal-title":"Anesthesia & Analgesia"},{"issue":"6","key":"2141_CR22","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TCBB.2019.2911071","volume":"17","author":"A Ogunleye","year":"2019","unstructured":"Ogunleye, A., & Wang, Q. G. (2019). XGBoost model for chronic kidney disease diagnosis. IEEE\/ACM transactions on computational biology and bioinformatics, 17(6), 2131-2140. https:\/\/doi.org\/10.1109\/TCBB.2019.2911071","journal-title":"IEEE\/ACM transactions on computational biology and bioinformatics"},{"key":"2141_CR23","doi-asserted-by":"publisher","first-page":"23366","DOI":"10.1109\/ACCESS.2023.3253885","volume":"11","author":"H Yang","year":"2023","unstructured":"Yang, H., Chen, Z., Yang, H., & Tian, M. (2023). Predicting coronary heart disease using an improved LightGBM model: Performance analysis and comparison. IEEE Access, 11, 23366-23380. https:\/\/doi.org\/10.1109\/ACCESS.2023.3253885","journal-title":"IEEE Access"},{"issue":"1","key":"2141_CR24","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: an interdisciplinary review. Journal of big data, 7(1), 94. https:\/\/doi.org\/10.1186\/s40537-020-00369-8","journal-title":"Journal of big data"},{"issue":"4","key":"2141_CR25","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.jamcollsurg.2019.05.029","volume":"229","author":"MA Bartek","year":"2019","unstructured":"Bartek, M. A., Saxena, R. C., Solomon, S., Fong, C. T., Behara, L. D., Venigandla, R., ... & Nair, B. G. (2019). Improving operating room efficiency: machine learning approach to predict case-time duration. Journal of the American College of Surgeons, 229(4), 346-354. https:\/\/doi.org\/10.1016\/j.jamcollsurg.2019.05.029","journal-title":"Journal of the American College of Surgeons"},{"issue":"4","key":"2141_CR26","doi-asserted-by":"publisher","first-page":"e176233","DOI":"10.1001\/jamasurg.2017.6233","volume":"153","author":"CP Childers","year":"2018","unstructured":"Childers, C. P., & Maggard-Gibbons, M. (2018). Understanding costs of care in the operating room. JAMA surgery, 153(4), e176233-e176233. https:\/\/doi.org\/10.1001\/jamasurg.2017.6233","journal-title":"JAMA surgery"},{"issue":"2","key":"2141_CR27","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.jamcollsurg.2014.10.021","volume":"220","author":"AM Stey","year":"2015","unstructured":"Stey, A. M., Brook, R. H., Needleman, J., Hall, B. L., Zingmond, D. S., Lawson, E. H., & Ko, C. Y. (2015). Hospital costs by cost center of inpatient hospitalization for medicare patients undergoing major abdominal surgery. Journal of the American College of Surgeons, 220(2), 207-217. https:\/\/doi.org\/10.1016\/j.jamcollsurg.2014.10.021","journal-title":"Journal of the American College of Surgeons"},{"issue":"2","key":"2141_CR28","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1053\/j.sempedsurg.2018.02.004","volume":"27","author":"DH Rothstein","year":"2018","unstructured":"Rothstein, D. H., & Raval, M. V. (2018). Operating room efficiency. In Seminars in pediatric surgery. Vol. 27, No. 2, pp. 79-85. https:\/\/doi.org\/10.1053\/j.sempedsurg.2018.02.004","journal-title":"In Seminars in pediatric surgery."},{"issue":"4","key":"2141_CR29","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1097\/EJA.0000000000000594","volume":"34","author":"C Aldecoa","year":"2017","unstructured":"Aldecoa, C., Bettelli, G., Bilotta, F., Sanders, R. D., Audisio, R., Borozdina, A., ... & Spies, C. D. (2017). European Society of Anaesthesiology evidence-based and consensus-based guideline on postoperative delirium. European Journal of Anaesthesiology| EJA, 34(4), 192-214. https:\/\/doi.org\/10.1097\/EJA.0000000000000594","journal-title":"European Journal of Anaesthesiology EJA"},{"issue":"9","key":"2141_CR30","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1097\/EJA.0000000000001684","volume":"39","author":"J Joris","year":"2022","unstructured":"Joris, J., Kehlet, H., & Slim, K. (2022). Postoperative cognitive dysfunction: time for enhanced recovery after surgery programmes. European Journal of Anaesthesiology| EJA, 39(9), 733-734. https:\/\/doi.org\/10.1097\/EJA.0000000000001684","journal-title":"European Journal of Anaesthesiology EJA"},{"issue":"3","key":"2141_CR31","doi-asserted-by":"publisher","first-page":"57","DOI":"10.4240\/wjgs.v2.i3.57","volume":"2","author":"JE de Aguilar-Nascimento","year":"2010","unstructured":"de Aguilar-Nascimento, J. E., & Dock-Nascimento, D. B. (2010). Reducing preoperative fasting time: A trend based on evidence. World Journal of Gastrointestinal Surgery, 2(3), 57. https:\/\/doi.org\/10.4240\/wjgs.v2.i3.57","journal-title":"World Journal of Gastrointestinal Surgery"},{"issue":"3","key":"2141_CR32","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1001\/jamasurg.2022.5867","volume":"158","author":"M Marsman","year":"2023","unstructured":"Marsman, M., Kappen, T. H., Vernooij, L. M., van der Hout, E. C., van Waes, J. A., & van Klei, W. A. (2023). Association of a liberal fasting policy of clear fluids before surgery with fasting duration and patient well-being and safety. JAMA surgery, 158(3), 254-263. https:\/\/doi.org\/10.1001\/jamasurg.2022.5867","journal-title":"JAMA surgery"},{"key":"2141_CR33","doi-asserted-by":"publisher","unstructured":"Huang, C. C., Lai, J., Cho, D. Y., & Yu, J. (2020). A machine learning study to improve surgical case duration prediction. https:\/\/doi.org\/10.21203\/rs.3.rs-40927\/v1","DOI":"10.21203\/rs.3.rs-40927\/v1"},{"issue":"1","key":"2141_CR34","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1186\/s12913-023-10264-6","volume":"23","author":"V Riahi","year":"2023","unstructured":"Riahi, V., Hassanzadeh, H., Khanna, S., Boyle, J., Syed, F., Biki, B., ... & Sweeney, L. (2023). Improving preoperative prediction of surgery duration. BMC Health Services Research, 23(1), 1343. https:\/\/doi.org\/10.1186\/s12913-023-10264-6","journal-title":"BMC Health Services Research"},{"issue":"2","key":"2141_CR35","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.joms.2012.10.009","volume":"71","author":"DM Laskin","year":"2013","unstructured":"Laskin, D. M., Abubaker, A. O., & Strauss, R. A. (2013). Accuracy of predicting the duration of a surgical operation. Journal of Oral and Maxillofacial Surgery, 71(2), 446-447. https:\/\/doi.org\/10.1016\/j.joms.2012.10.009","journal-title":"Journal of Oral and Maxillofacial Surgery"},{"issue":"6","key":"2141_CR36","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1097\/00000542-199612000-00003","volume":"85","author":"IH Wright","year":"1996","unstructured":"Wright, I. H., Kooperberg, C., Bonar, B. A., & Bashein, G. (1996). Statistical modeling to predict elective surgery time: comparison with a computer scheduling system and surgeon-provided estimates. The Journal of the American Society of Anesthesiologists, 85(6), 1235-1245. https:\/\/doi.org\/10.1097\/00000542-199612000-00003","journal-title":"The Journal of the American Society of Anesthesiologists"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02141-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02141-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02141-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T20:12:47Z","timestamp":1736885567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02141-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,14]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2141"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02141-y","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,14]]},"assertion":[{"value":"28 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was approved by the Institutional Review Board (IRB) of Seoul National University Hospital, Seoul, Korea, (Number 2306\u2013164-1443; Date of approval, 29 June 2023). The requirement for written informed patient consent was waived by the IRB owing to the retrospective nature of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"8"}}