{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T10:28:53Z","timestamp":1787048933427,"version":"build-2736575974"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Res"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1038\/s41390-025-04368-4","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T16:15:08Z","timestamp":1757607308000},"page":"2590-2597","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intradialytic hypotension and hemodynamic phenotypes in children following continuous renal replacement therapy initiation"],"prefix":"10.1038","volume":"99","author":[{"given":"Sameer","family":"Thadani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christin","family":"Silos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Horvat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin","family":"Dolan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Poyyapakkam","family":"Srivaths","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Fogarty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayse","family":"Akcan-Arikan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier A.","family":"Neyra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"4368_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1056\/NEJMoa1611391","volume":"376","author":"A Kaddourah","year":"2017","unstructured":"Kaddourah, A., Basu, R. K., Bagshaw, S. M. & Goldstein, S. L. & AWARE Investigators. Epidemiology of Acute Kidney Injury in Critically Ill Children and Young Adults. N. Engl. J. Med. 376, 11\u201320 (2017).","journal-title":"N. Engl. J. Med."},{"key":"4368_CR2","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/S2352-4642(17)30069-X","volume":"1","author":"JG Jetton","year":"2017","unstructured":"Jetton, J. G. et al. Incidence and outcomes of neonatal acute kidney injury (AWAKEN): a multicentre, multinational, observational cohort study. Lancet Child Adolesc. Health 1, 184\u2013194 (2017).","journal-title":"Lancet Child Adolesc. Health"},{"key":"4368_CR3","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.2215\/CJN.08250718","volume":"14","author":"R Chanchlani","year":"2019","unstructured":"Chanchlani, R. et al. Secular trends in incidence, modality and mortality with dialysis receiving AKI in children in Ontario: A Population-Based Cohort Study. Clin. J. Am. Soc. Nephrol. 14, 1288\u20131296 (2019).","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"4368_CR4","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.2020111665","author":"C Robinson","year":"2021","unstructured":"Robinson, C. et al. Long-term kidney outcomes following childhood acute kidney injury receiving dialysis: a population-based cohort study. JASN Asn. 2020111665 https:\/\/doi.org\/10.1681\/ASN.2020111665 (2021).","journal-title":"JASN Asn. 2020111665"},{"key":"4368_CR5","doi-asserted-by":"publisher","first-page":"381","DOI":"10.2174\/157488812802481445","volume":"7","author":"L Elbahlawan","year":"2012","unstructured":"Elbahlawan, L. & Morrison, R. R. Continuous renal replacement therapy in children post-hematopoietic stem cell transplantation: the present and the future. Curr. Stem Cell Res Ther. 7, 381\u2013387 (2012).","journal-title":"Curr. Stem Cell Res Ther."},{"key":"4368_CR6","doi-asserted-by":"publisher","unstructured":"Starr, M. C. et al. Continuous Kidney Replacement Therapy and Survival in Children and Young Adults: Findings From the Multinational WE-ROCK Collaborative. Am. J. Kidney Dis. S0272-6386(24)00610\u20133 (2024) https:\/\/doi.org\/10.1053\/j.ajkd.2023.12.017.","DOI":"10.1053\/j.ajkd.2023.12.017"},{"key":"4368_CR7","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.0243","volume":"7","author":"DY Fuhrman","year":"2024","unstructured":"Fuhrman, D. Y. et al. Major adverse kidney events in pediatric continuous kidney replacement therapy. JAMA Netw. Open 7, e240243 (2024).","journal-title":"JAMA Netw. Open"},{"key":"4368_CR8","first-page":"e1084","volume":"6","author":"S Thadani","year":"2024","unstructured":"Thadani, S. et al. Patterns of multiple organ dysfunction and renal recovery in critically ill children and young adults receiving continuous renal replacement therapy. Crit. Care Explor 6, e1084 (2024).","journal-title":"Crit. Care Explor"},{"key":"4368_CR9","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1053\/j.ajkd.2009.10.048","volume":"55","author":"SM Sutherland","year":"2010","unstructured":"Sutherland, S. M. et al. Fluid overload and mortality in children receiving continuous renal replacement therapy: the prospective pediatric continuous renal replacement therapy registry. Am. J. Kidney Dis. 55, 316\u2013325 (2010).","journal-title":"Am. J. Kidney Dis."},{"key":"4368_CR10","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.49871","volume":"7","author":"KM Gist","year":"2024","unstructured":"Gist, K. M. et al. Time to continuous renal replacement therapy initiation and 90-day major adverse kidney events in children and young adults. JAMA Netw. Open 7, e2349871 (2024).","journal-title":"JAMA Netw. Open"},{"key":"4368_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/cc8172","volume":"13","author":"MJ Santiago","year":"2009","unstructured":"Santiago, M. J. et al. Complications of continuous renal replacement therapy in critically ill children: a prospective observational evaluation study. Crit. Care 13, R184 (2009).","journal-title":"Crit. Care"},{"key":"4368_CR12","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":"4368_CR13","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1681\/ASN.2014020222","volume":"26","author":"JE Flythe","year":"2015","unstructured":"Flythe, J. E., Xue, H., Lynch, K. E., Curhan, G. C. & Brunelli, S. M. Association of Mortality Risk with Various Definitions of Intradialytic Hypotension. J. Am. Soc. Nephrol. 26, 724\u2013734 (2015).","journal-title":"J. Am. Soc. Nephrol."},{"key":"4368_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.jcrc.2019.07.004","volume":"54","author":"K Shawwa","year":"2019","unstructured":"Shawwa, K. et al. Hypotension within one-hour from starting CRRT is associated with in-hospital mortality. J. Crit. Care 54, 7\u201313 (2019).","journal-title":"J. Crit. Care"},{"key":"4368_CR15","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1007\/s00134-007-0754-4","volume":"33","author":"S Uchino","year":"2007","unstructured":"Uchino, S. et al. Continuous renal replacement therapy: A worldwide practice survey: The Beginning and Ending Supportive Therapy for the Kidney (B.E.S.T. Kidney) Investigators. Intensive Care Med. 33, 1563\u20131570 (2007).","journal-title":"Intensive Care Med."},{"key":"4368_CR16","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1159\/000380903","volume":"39","author":"A Akhoundi","year":"2015","unstructured":"Akhoundi, A. et al. Incidence of adverse events during continuous renal replacement therapy. Blood Purif. 39, 333\u2013339 (2015).","journal-title":"Blood Purif."},{"key":"4368_CR17","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s00467-018-4190-1","volume":"34","author":"R Raina","year":"2019","unstructured":"Raina, R. et al. Pediatric intradialytic hypotension: recommendations from the Pediatric Continuous Renal Replacement Therapy (PCRRT) Workgroup. Pediatr. Nephrol. 34, 925\u2013941 (2019).","journal-title":"Pediatr. Nephrol."},{"key":"4368_CR18","doi-asserted-by":"publisher","first-page":"2167","DOI":"10.1007\/s00467-022-05424-5","volume":"37","author":"S Thadani","year":"2022","unstructured":"Thadani, S. et al. Hemodynamic instability during connection to continuous kidney replacement therapy in critically ill pediatric patients. Pediatr. Nephrol. 37, 2167\u20132177 (2022).","journal-title":"Pediatr. Nephrol."},{"key":"4368_CR19","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1111\/aor.13086","volume":"42","author":"SN Fernandez Lafever","year":"2018","unstructured":"Fernandez Lafever, S. N. et al. Hemodynamic effects of connection to continuous renal replacement therapy in a pediatric animal model. Artif. Organs 42, 640\u2013646 (2018).","journal-title":"Artif. Organs"},{"key":"4368_CR20","first-page":"012012","volume":"1142","author":"J Alzubi","year":"2018","unstructured":"Alzubi, J., Nayyar, A. & Kumar, A. Machine learning from theory to algorithms: an overview. J. Phys.: Conf. Ser. 1142, 012012 (2018).","journal-title":"J. Phys.: Conf. Ser."},{"key":"4368_CR21","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s00167-022-07233-7","volume":"31","author":"CM Eckhardt","year":"2023","unstructured":"Eckhardt, C. M. et al. Unsupervised machine learning methods and emerging applications in healthcare. Knee Surg. Sports Traumatol. Arthrosc. 31, 376\u2013381 (2023).","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"4368_CR22","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","volume":"51","author":"T Jiang","year":"2020","unstructured":"Jiang, T., Gradus, J. L. & Rosellini, A. J. Supervised machine learning: a brief primer. Behav. Ther. 51, 675\u2013687 (2020).","journal-title":"Behav. Ther."},{"key":"4368_CR23","first-page":"e1188","volume":"6","author":"S Thadani","year":"2024","unstructured":"Thadani, S. et al. Machine learning-based prediction model for ICU mortality after continuous renal replacement therapy initiation in children. Crit. Care Explor.6, e1188 (2024).","journal-title":"Crit. Care Explor."},{"key":"4368_CR24","doi-asserted-by":"publisher","unstructured":"Dowrick, J. M. et al. Unsupervised machine learning highlights the challenges of subtyping disorders of gut-brain interaction. Neurogastroenterol. Motil e14898 (2024) https:\/\/doi.org\/10.1111\/nmo.14898.","DOI":"10.1111\/nmo.14898"},{"key":"4368_CR25","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.is.2015.04.007","volume":"53","author":"S Aghabozorgi","year":"2015","unstructured":"Aghabozorgi, S., Seyed Shirkhorshidi, A. & Ying Wah, T. Time-series clustering \u2013 A decade review. Inf. Syst. 53, 16\u201338 (2015).","journal-title":"Inf. Syst."},{"key":"4368_CR26","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1097\/PCC.0000000000001191","volume":"18","author":"AM McIntosh","year":"2017","unstructured":"McIntosh, A. M., Tong, S., Deakyne, S. J., Davidson, J. A. & Scott, H. F. Validation of the vasoactive-inotropic score in pediatric sepsis*. Pediatr. Crit. Care Med. 18, 750\u2013757 (2017).","journal-title":"Pediatr. Crit. Care Med."},{"key":"4368_CR27","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1097\/PCC.0b013e3181b806fc","volume":"11","author":"MG Gaies","year":"2010","unstructured":"Gaies, M. G. et al. Vasoactive\u2013inotropic score as a predictor of morbidity and mortality in infants after cardiopulmonary bypass*. Pediatr. Crit. Care Med. 11, 234\u2013238 (2010).","journal-title":"Pediatr. Crit. Care Med."},{"key":"4368_CR28","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-015-1054-y","volume":"19","author":"S Leteurtre","year":"2015","unstructured":"Leteurtre, S. et al. Daily estimation of the severity of organ dysfunctions in critically ill children by using the PELOD-2 score. Crit. Care 19, 324 (2015).","journal-title":"Crit. Care"},{"key":"4368_CR29","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1097\/CCM.0b013e31828a2bbd","volume":"41","author":"S Leteurtre","year":"2013","unstructured":"Leteurtre, S. et al. PELOD-2: an update of the PEdiatric logistic organ dysfunction score. Crit. Care Med. 41, 1761\u20131773 (2013).","journal-title":"Crit. Care Med."},{"key":"4368_CR30","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1542\/peds.58.2.259","volume":"58","author":"GJ Schwartz","year":"1976","unstructured":"Schwartz, G. J., Haycock, G. B., Edelmann, C. M. & Spitzer, A. A simple estimate of glomerular filtration rate in children derived from body length and plasma creatinine. Pediatrics 58, 259\u2013263 (1976).","journal-title":"Pediatrics"},{"key":"4368_CR31","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1159\/000363725","volume":"127","author":"FT Billings IV","year":"2014","unstructured":"Billings, F. T. IV & Shaw, A. D. Clinical trial endpoints in acute kidney injury. Nephron Clin. Pr. 127, 89\u201393 (2014).","journal-title":"Nephron Clin. Pr."},{"key":"4368_CR32","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1093\/jamia\/ocae121","volume":"31","author":"DS Carrell","year":"2024","unstructured":"Carrell, D. S. et al. A general framework for developing computable clinical phenotype algorithms. J. Am. Med. Inform. Assoc. 31, 1785\u20131796 (2024).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"4368_CR33","doi-asserted-by":"publisher","unstructured":"Desai, R. J. et al. Process guide for inferential studies using healthcare data from routine clinical practice to evaluate causal effects of drugs (PRINCIPLED): considerations from the FDA Sentinel Innovation Center. BMJ e076460 (2024) https:\/\/doi.org\/10.1136\/bmj-2023-076460.","DOI":"10.1136\/bmj-2023-076460"},{"key":"4368_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s11306-018-1451-8","volume":"14","author":"JY Lee","year":"2018","unstructured":"Lee, J. Y. & Styczynski, M. P. N. S. - kNN: a modified k-nearest neighbors approach for imputing metabolomics data. Metabolomics 14, 153 (2018).","journal-title":"Metabolomics"},{"key":"4368_CR35","first-page":"1372","volume":"50","author":"JH Lee","year":"2021","unstructured":"Lee, J. H. & Huber, J. C. Evaluation of multiple imputation with large proportions of missing data: how much is too much?. Iran. J. Public Health 50, 1372\u20131380 (2021).","journal-title":"Iran. J. Public Health"},{"key":"4368_CR36","doi-asserted-by":"publisher","first-page":"e759","DOI":"10.1097\/PCC.0000000000002495","volume":"21","author":"JS Roberts","year":"2020","unstructured":"Roberts, J. S., Yanay, O. & Barry, D. Age-based percentiles of measured mean arterial pressure in pediatric patients in a hospital setting. Pediatr. Crit. Care Med. 21, e759\u2013e768 (2020).","journal-title":"Pediatr. Crit. Care Med."},{"key":"4368_CR37","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1097\/01.PCC.0000257039.32593.DC","volume":"8","author":"IU Haque","year":"2007","unstructured":"Haque, I. U. & Zaritsky, A. L. Analysis of the evidence for the lower limit of systolic and mean arterial pressure in children. Pediatr. Crit. Care Med. 8, 138\u2013144 (2007).","journal-title":"Pediatr. Crit. Care Med."},{"key":"4368_CR38","doi-asserted-by":"publisher","unstructured":"Subasi, A. Clustering examples. In Practical Machine Learning for Data Analysis Using Python 465\u2013511 (Elsevier, 2020). https:\/\/doi.org\/10.1016\/B978-0-12-821379-7.00007-2.","DOI":"10.1016\/B978-0-12-821379-7.00007-2"},{"key":"4368_CR39","doi-asserted-by":"publisher","unstructured":"Niennattrakul, V. & Ratanamahatana, C. A. Clustering multimedia data using time series. In 2006 International Conference on Hybrid Information Technology 372\u2013379 (IEEE, Cheju Island, 2006). https:\/\/doi.org\/10.1109\/ICHIT.2006.253514.","DOI":"10.1109\/ICHIT.2006.253514"},{"key":"4368_CR40","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1037\/met0000049","volume":"21","author":"D Steinley","year":"2016","unstructured":"Steinley, D., Brusco, M. J. & Hubert, L. The variance of the adjusted Rand index. Psychol. Methods 21, 261\u2013272 (2016).","journal-title":"Psychol. Methods"},{"key":"4368_CR41","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1093\/ckj\/sfaa078","volume":"13","author":"M Kanbay","year":"2020","unstructured":"Kanbay, M. et al. An update review of intradialytic hypotension: concept, risk factors, clinical implications and management. Clin. Kidney J. 13, 981\u2013993 (2020).","journal-title":"Clin. Kidney J."},{"key":"4368_CR42","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1159\/000503776","volume":"49","author":"B Sars","year":"2020","unstructured":"Sars, B., van der Sande, F. M. & Kooman, J. P. Intradialytic hypotension: mechanisms and outcome. Blood Purif. 49, 158\u2013167 (2020).","journal-title":"Blood Purif."},{"key":"4368_CR43","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1111\/j.1525-139X.2007.00281.x","volume":"20","author":"NM Selby","year":"2007","unstructured":"Selby, N. M. & McIntyre, C. W. The acute cardiac effects of dialysis. Semin Dial. 20, 220\u2013228 (2007).","journal-title":"Semin Dial."},{"key":"4368_CR44","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jpeds.2019.11.019","volume":"220","author":"S Gubb","year":"2020","unstructured":"Gubb, S. et al. Acute kidney injury in children based on electronic alerts. J. Pediatr.220, 14\u201320.e4 (2020).","journal-title":"J. Pediatr."},{"key":"4368_CR45","doi-asserted-by":"publisher","DOI":"10.1186\/s40001-023-01455-2","volume":"28","author":"X Kong","year":"2023","unstructured":"Kong, X. et al. Acute renal injury after aortic arch reconstruction with cardiopulmonary bypass for children: prediction models by machine learning of a retrospective cohort study. Eur. J. Med. Res. 28, 499 (2023).","journal-title":"Eur. J. Med. Res."},{"key":"4368_CR46","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-021-03724-0","volume":"25","author":"J Dong","year":"2021","unstructured":"Dong, J. et al. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care. Crit. Care 25, 288 (2021).","journal-title":"Crit. Care"},{"key":"4368_CR47","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s44158-023-00125-3","volume":"3","author":"T Fragasso","year":"2023","unstructured":"Fragasso, T. et al. Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit. J. Anesth. Analg. Crit. Care 3, 37 (2023).","journal-title":"J. Anesth. Analg. Crit. Care"},{"key":"4368_CR48","doi-asserted-by":"publisher","unstructured":"Cama-Olivares, A. et al. Systematic review and meta-analysis of machine learning models for acute kidney injury risk classification. J. Am. Soc. Nephrol. (2025) https:\/\/doi.org\/10.1681\/ASN.0000000702.","DOI":"10.1681\/ASN.0000000702"},{"key":"4368_CR49","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1159\/000539787","volume":"53","author":"S Zhu","year":"2024","unstructured":"Zhu, S. et al. Machine learning-aided decision-making model for the discontinuation of continuous renal replacement therapy. Blood Purif. 53, 704\u2013715 (2024).","journal-title":"Blood Purif."},{"key":"4368_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-96727-4","volume":"11","author":"MW Kang","year":"2021","unstructured":"Kang, M. W. et al. Machine learning model to predict hypotension after starting continuous renal replacement therapy. Sci. Rep. 11, 17169 (2021).","journal-title":"Sci. Rep."},{"key":"4368_CR51","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.3390\/diagnostics12061496","volume":"12","author":"P-S Hung","year":"2022","unstructured":"Hung, P.-S. et al. Explainable machine learning-based risk prediction model for in-hospital mortality after continuous renal replacement therapy initiation. Diagnostics 12, 1496 (2022).","journal-title":"Diagnostics"},{"key":"4368_CR52","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-020-2752-7","volume":"24","author":"MW Kang","year":"2020","unstructured":"Kang, M. W. et al. Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit. Care 24, 42 (2020).","journal-title":"Crit. Care"},{"key":"4368_CR53","first-page":"529","volume":"29","author":"A Dahiya","year":"2023","unstructured":"Dahiya, A., Pannu, N. & Soranno, D. E. Sex as a biological variable in acute kidney injury. Curr. Opin. Crit. Care 29, 529\u2013533 (2023).","journal-title":"Curr. Opin. Crit. Care"},{"key":"4368_CR54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24876-0","volume":"8","author":"M Peikari","year":"2018","unstructured":"Peikari, M., Salama, S., Nofech-Mozes, S. & Martel, A. L. A cluster-then-label semi-supervised learning approach for pathology image classification. Sci. Rep. 8, 7193 (2018).","journal-title":"Sci. Rep."},{"key":"4368_CR55","doi-asserted-by":"publisher","first-page":"671","DOI":"10.2215\/CJN.0000000000000099","volume":"18","author":"LJ Liu","year":"2023","unstructured":"Liu, L. J., Takeuchi, T., Chen, J. & Neyra, J. A. Artificial Intelligence in Continuous Kidney Replacement Therapy. Clin. J. Am. Soc. Nephrol. 18, 671\u2013674 (2023).","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"4368_CR56","doi-asserted-by":"publisher","first-page":"e195418","DOI":"10.1001\/jamanetworkopen.2019.5418","volume":"2","author":"R Murugan","year":"2019","unstructured":"Murugan, R. et al. Association of net ultrafiltration rate with mortality among critically ill adults with acute kidney injury receiving continuous venovenous hemodiafiltration: a secondary analysis of the Randomized Evaluation of Normal vs Augmented Level (RENAL) of renal replacement therapy trial. JAMA Netw. Open 2, e195418 (2019).","journal-title":"JAMA Netw. Open"}],"container-title":["Pediatric Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41390-025-04368-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41390-025-04368-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41390-025-04368-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T10:00:56Z","timestamp":1787047256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41390-025-04368-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":56,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["4368"],"URL":"https:\/\/doi.org\/10.1038\/s41390-025-04368-4","relation":{},"ISSN":["0031-3998","1530-0447"],"issn-type":[{"value":"0031-3998","type":"print"},{"value":"1530-0447","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"25 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Given the retrospective nature of the study, the Institutional Review Board approved the protocol and waived the requirement for informed patient consent.","order":2,"name":"Ethics","label":"Patient consent","group":{"name":"EthicsHeading","label":"Ethics"}}]}}