{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T16:21:39Z","timestamp":1780503699775,"version":"3.54.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01863-0","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T05:12:12Z","timestamp":1753333932000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL)"],"prefix":"10.1038","volume":"8","author":[{"given":"Lingzhong","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangqiong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhua","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuotian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ameya D.","family":"Parab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aishwarya","family":"Budhkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saravanan","family":"Kanakasabai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David C.","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyue","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"1863_CR1","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1097\/00003246-198906000-00009","volume":"17","author":"JL Vincent","year":"1989","unstructured":"Vincent, J. L., Parquier, J. N., Preiser, J. C., Brimioulle, S. & Kahn, R. J. Terminal events in the intensive care unit: review of 258 fatal cases in one year. Crit. Care Med. 17, 530\u2013533 (1989).","journal-title":"Crit. Care Med."},{"key":"1863_CR2","doi-asserted-by":"publisher","first-page":"178","DOI":"10.12816\/0014441","volume":"37","author":"MAK Omar","year":"2015","unstructured":"Omar, M. A. K., Aram, F. O. & Banafa, N. S. Causes of mortality among critically ill patients admitted in intensive care unit. Bahrain Med. Bull. 37, 178\u2013180 (2015).","journal-title":"Bahrain Med. Bull."},{"key":"1863_CR3","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1001\/jama.2013.278477","volume":"310","author":"A Morelli","year":"2013","unstructured":"Morelli, A. et al. Effect of heart rate control with Esmolol on hemodynamic and clinical outcomes in patients with septic shock: a randomized clinical trial. JAMA 310, 1683\u20131691 (2013).","journal-title":"JAMA"},{"key":"1863_CR4","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s00134-018-5218-5","volume":"44","author":"K Maheshwari","year":"2018","unstructured":"Maheshwari, K. et al. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Med. 44, 857\u2013867 (2018).","journal-title":"Intensive Care Med."},{"key":"1863_CR5","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.bja.2021.06.048","volume":"127","author":"L Meng","year":"2021","unstructured":"Meng, L. Heterogeneous impact of hypotension on organ perfusion and outcomes: a narrative review. Br. J. Anaesth. 127, 845\u2013861 (2021).","journal-title":"Br. J. Anaesth."},{"key":"1863_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-022-04289-2","volume":"27","author":"PJ McGuigan","year":"2023","unstructured":"McGuigan, P. J. et al. The effect of blood pressure on mortality following out-of-hospital cardiac arrest: a retrospective cohort study of the United Kingdom Intensive Care National Audit and Research Centre database. Crit. Care 27, 4 (2023).","journal-title":"Crit. Care"},{"key":"1863_CR7","doi-asserted-by":"publisher","first-page":"e03491","DOI":"10.1016\/j.heliyon.2020.e03491","volume":"6","author":"D Nguyen","year":"2020","unstructured":"Nguyen, D., Kritek, P. A., Greco, S. A. & Prutkin, J. M. Bradycardia at the onset of pulseless electrical activity arrests in hospitalized patients is associated with improved survival to discharge. Heliyon 6, e03491 (2020).","journal-title":"Heliyon"},{"key":"1863_CR8","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1093\/intqhc\/mzv077","volume":"27","author":"J Yong","year":"2015","unstructured":"Yong, J., Hibbert, P., Runciman, W. B. & Coventry, B. J. Bradycardia as an early warning sign for cardiac arrest during routine laparoscopic surgery. Int. J. Qual. Health Care 27, 473\u2013478 (2015).","journal-title":"Int. J. Qual. Health Care"},{"key":"1863_CR9","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1056\/NEJMoa1312173","volume":"370","author":"P Asfar","year":"2014","unstructured":"Asfar, P. et al. High versus low blood-pressure target in patients with septic shock. N. Engl. J. Med. 370, 1583\u20131593 (2014).","journal-title":"N. Engl. J. Med."},{"key":"1863_CR10","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/j.mcna.2020.02.011","volume":"104","author":"MD Font","year":"2020","unstructured":"Font, M. D., Thyagarajan, B. & Khanna, A. K. Sepsis and Septic Shock - Basics of diagnosis, pathophysiology and clinical decision making. Med. Clin. North Am. 104, 573\u2013585 (2020).","journal-title":"Med. Clin. North Am."},{"key":"1863_CR11","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s11739-021-02735-7","volume":"16","author":"F Gavelli","year":"2021","unstructured":"Gavelli, F., Castello, L. M. & Avanzi, G. C. Management of sepsis and septic shock in the emergency department. Intern Emerg. Med. 16, 1649\u20131661 (2021).","journal-title":"Intern Emerg. Med."},{"key":"1863_CR12","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082104","volume":"388","author":"J Han","year":"2025","unstructured":"Han, J. et al. Care guided by tissue oxygenation and haemodynamic monitoring in off-pump coronary artery bypass grafting (Bottomline-CS): assessor blind, single centre, randomised controlled trial. BMJ 388, e082104 (2025).","journal-title":"BMJ"},{"key":"1863_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-022-04255-y","volume":"26","author":"D De Backer","year":"2022","unstructured":"De Backer, D. et al. A plea for personalization of the hemodynamic management of septic shock. Crit. Care 26, 372 (2022).","journal-title":"Crit. Care"},{"key":"1863_CR14","first-page":"e547","volume":"3","author":"S Sharma","year":"2021","unstructured":"Sharma, S. et al. ICU mortality in patients with Coronavirus disease 2019 infection: highlighting healthcare disparities in rural Appalachia. Crit. Care Explor 3, e547 (2021).","journal-title":"Crit. Care Explor"},{"key":"1863_CR15","doi-asserted-by":"publisher","DOI":"10.1002\/hsr2.1634","volume":"6","author":"SH Khan","year":"2023","unstructured":"Khan, S. H. et al. Development of a population-level prediction model for intensive care unit (ICU) survivorship and mortality in older adults: A population-based cohort study. Health Sci. Rep. 6, e1634 (2023).","journal-title":"Health Sci. Rep."},{"key":"1863_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard, T. J. et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci. Data 5, 180178 (2018).","journal-title":"Sci. Data"},{"key":"1863_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).","journal-title":"Sci. Data"},{"key":"1863_CR18","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-022-04173-z","volume":"26","author":"MR Pinsky","year":"2022","unstructured":"Pinsky, M. R. et al. Effective hemodynamic monitoring. Crit. Care 26, 294 (2022).","journal-title":"Crit. Care"},{"key":"1863_CR19","doi-asserted-by":"publisher","first-page":"e179","DOI":"10.1016\/S2589-7500(20)30018-2","volume":"2","author":"HC Thorsen-Meyer","year":"2020","unstructured":"Thorsen-Meyer, H. C. et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digit Health 2, e179\u2013e191 (2020).","journal-title":"Lancet Digit Health"},{"key":"1863_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-67952-0","volume":"10","author":"B Chan","year":"2020","unstructured":"Chan, B. et al. Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach. Sci. Rep. 10, 11480 (2020).","journal-title":"Sci. Rep."},{"key":"1863_CR21","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1038\/s41746-022-00679-6","volume":"5","author":"HC Thorsen-Meyer","year":"2022","unstructured":"Thorsen-Meyer, H. C. et al. Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data. NPJ Digit. Med. 5, 142 (2022).","journal-title":"NPJ Digit. Med."},{"key":"1863_CR22","doi-asserted-by":"crossref","unstructured":"Alves, T., Laender, A., Veloso, A. & Ziviani, N. Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation. in 2018 IEEE International Conference on Big Data (Big Data) 1328-1336 (2018).","DOI":"10.1109\/BigData.2018.8621927"},{"key":"1863_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-25472-z","volume":"12","author":"BP Bednarski","year":"2022","unstructured":"Bednarski, B. P. et al. Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction. Sci. Rep. 12, 21247 (2022).","journal-title":"Sci. Rep."},{"key":"1863_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-79142-z","volume":"10","author":"J Deasy","year":"2020","unstructured":"Deasy, J., Lio, P. & Ercole, A. Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation. Sci. Rep. 10, 22129 (2020).","journal-title":"Sci. Rep."},{"key":"1863_CR25","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.annemergmed.2020.11.007","volume":"77","author":"G Wardi","year":"2021","unstructured":"Wardi, G. et al. Predicting progression to septic shock in the emergency department using an externally generalizable machine-learning algorithm. Ann. Emerg. Med. 77, 395\u2013406 (2021).","journal-title":"Ann. Emerg. Med."},{"key":"1863_CR26","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1097\/CCM.0000000000002936","volume":"46","author":"S Nemati","year":"2018","unstructured":"Nemati, S. et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit. Care Med. 46, 547\u2013553 (2018).","journal-title":"Crit. Care Med."},{"key":"1863_CR27","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ijnurstu.2018.10.008","volume":"93","author":"H Fan","year":"2019","unstructured":"Fan, H. et al. Development and validation of a dynamic delirium prediction rule in patients admitted to the Intensive Care Units (DYNAMIC-ICU): A prospective cohort study. Int J. Nurs. Stud. 93, 64\u201373 (2019).","journal-title":"Int J. Nurs. Stud."},{"key":"1863_CR28","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/j.cell.2019.06.011","volume":"178","author":"DM Kurtz","year":"2019","unstructured":"Kurtz, D. M. et al. Dynamic risk profiling using serial tumor biomarkers for personalized outcome prediction. Cell 178, 699\u2013713.e619 (2019).","journal-title":"Cell"},{"key":"1863_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-98387-w","volume":"11","author":"Y Gu","year":"2021","unstructured":"Gu, Y. et al. Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data. Sci. Rep. 11, 18961 (2021).","journal-title":"Sci. Rep."},{"key":"1863_CR30","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41746-020-00338-8","volume":"3","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y. et al. Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study. NPJ Digit Med. 3, 135 (2020).","journal-title":"NPJ Digit Med."},{"key":"1863_CR31","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1038\/s44220-024-00237-x","volume":"2","author":"M Tanveer","year":"2024","unstructured":"Tanveer, M. et al. Ensemble deep learning for Alzheimer\u2019s disease characterization and estimation. Nat. Ment. Health 2, 655\u2013667 (2024).","journal-title":"Nat. Ment. Health"},{"key":"1863_CR32","doi-asserted-by":"crossref","unstructured":"Biderman, D. et al. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. bioRxiv (2024).","DOI":"10.1101\/2023.04.28.538703"},{"key":"1863_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-47899-w","volume":"15","author":"H Peng","year":"2024","unstructured":"Peng, H., Wang, H., Kong, W., Li, J. & Goh, W. W. B. Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference. Nat. Commun. 15, 3922 (2024).","journal-title":"Nat. Commun."},{"key":"1863_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107577","volume":"166","author":"W Hu","year":"2023","unstructured":"Hu, W. et al. An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM. Comput Biol. Med. 166, 107577 (2023).","journal-title":"Comput Biol. Med."},{"key":"1863_CR35","doi-asserted-by":"publisher","first-page":"3938492","DOI":"10.1155\/2022\/3938492","volume":"2022","author":"N Ren","year":"2022","unstructured":"Ren, N., Zhao, X. & Zhang, X. Mortality prediction in ICU using a stacked ensemble model. Comput Math. Methods Med. 2022, 3938492 (2022).","journal-title":"Comput Math. Methods Med."},{"key":"1863_CR36","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/9438046","volume":"2018","author":"M Toptas","year":"2018","unstructured":"Toptas, M. et al. Factors affecting the length of stay in the intensive care unit: our clinical experience. Biomed. Res. Int. 2018, 9438046 (2018).","journal-title":"Biomed. Res. Int."},{"key":"1863_CR37","doi-asserted-by":"publisher","DOI":"10.1186\/cc3921","volume":"10","author":"M Resche-Rigon","year":"2006","unstructured":"Resche-Rigon, M., Azoulay, E. & Chevret, S. Evaluating mortality in intensive care units: contribution of competing risks analyses. Crit. Care 10, R5 (2006).","journal-title":"Crit. Care"},{"key":"1863_CR38","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1093\/aje\/154.4.366","volume":"154","author":"JM Williamson","year":"2001","unstructured":"Williamson, J. M., Satten, G. A., Hanson, J. A., Weinstock, H. & Datta, S. Analysis of dynamic cohort data. Am. J. Epidemiol. 154, 366\u2013372 (2001).","journal-title":"Am. J. Epidemiol."},{"key":"1863_CR39","doi-asserted-by":"publisher","DOI":"10.1186\/cc5948","volume":"11","author":"JE Sevransky","year":"2007","unstructured":"Sevransky, J. E. et al. Hemodynamic goals in randomized clinical trials in patients with sepsis: a systematic review of the literature. Crit. Care 11, R67 (2007).","journal-title":"Crit. Care"},{"key":"1863_CR40","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1093\/ndt\/gfy283","volume":"34","author":"EL Fu","year":"2018","unstructured":"Fu, E. L. et al. Merits and caveats of propensity scores to adjust for confounding. Nephrol. Dial. Transplant. 34, 1629\u20131635 (2018).","journal-title":"Nephrol. Dial. Transplant."},{"key":"1863_CR41","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.lers.2024.02.001","volume":"7","author":"J Yang","year":"2024","unstructured":"Yang, J. et al. Identification of clinical subphenotypes of sepsis after laparoscopic surgery. Laparosc., Endosc. Robot. Surg. 7, 16\u201326 (2024).","journal-title":"Laparosc., Endosc. Robot. Surg."},{"key":"1863_CR42","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.lers.2022.10.002","volume":"5","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z. et al. Causal inference with marginal structural modeling for longitudinal data in laparoscopic surgery: A technical note. Laparosc., Endosc. Robot.Surg. 5, 146\u2013152 (2022).","journal-title":"Laparosc., Endosc. Robot.Surg."},{"key":"1863_CR43","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1001\/jama.2019.5791","volume":"321","author":"CW Seymour","year":"2019","unstructured":"Seymour, C. W. et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 321, 2003\u20132017 (2019).","journal-title":"JAMA"},{"key":"1863_CR44","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-019-2372-2","volume":"23","author":"A Leligdowicz","year":"2019","unstructured":"Leligdowicz, A. & Matthay, M. A. Heterogeneity in sepsis: new biological evidence with clinical applications. Crit. Care 23, 80 (2019).","journal-title":"Crit. Care"},{"key":"1863_CR45","doi-asserted-by":"publisher","first-page":"e581","DOI":"10.1097\/CCM.0000000000005517","volume":"50","author":"CM Sauer","year":"2022","unstructured":"Sauer, C. M. et al. Systematic review and comparison of publicly available ICU Data Sets\u2014A decision guide for clinicians and data scientists. Crit. Care Med. 50, e581\u2013e588 (2022).","journal-title":"Crit. Care Med."},{"key":"1863_CR46","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1093\/aje\/kwq433","volume":"173","author":"H Quan","year":"2011","unstructured":"Quan, H. et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am. J. Epidemiol. 173, 676\u2013682 (2011).","journal-title":"Am. J. Epidemiol."},{"key":"1863_CR47","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1159\/000521288","volume":"91","author":"ME Charlson","year":"2022","unstructured":"Charlson, M. E., Carrozzino, D., Guidi, J. & Patierno, C. Charlson Comorbidity Index: A critical review of clinimetric properties. Psychother. Psychosom. 91, 8\u201335 (2022).","journal-title":"Psychother. Psychosom."},{"key":"1863_CR48","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1097\/00003246-199811000-00016","volume":"26","author":"JL Vincent","year":"1998","unstructured":"Vincent, J. L. et al. Use of the SOFA score to assess the incidence of organ dysfunction\/failure in intensive care units: results of a multicenter, prospective study. Working group on \u201csepsis-related problems\u201d of the European Society of Intensive Care Medicine. Crit. Care Med. 26, 1793\u20131800 (1998).","journal-title":"Crit. Care Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01863-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01863-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01863-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T23:19:03Z","timestamp":1757287143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01863-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1863"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01863-0","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]},"assertion":[{"value":"15 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"474"}}