{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T08:16:59Z","timestamp":1787818619373,"version":"build-2784847793"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012416","name":"Inova Health System","doi-asserted-by":"publisher","award":["GRT1000000237"],"award-info":[{"award-number":["GRT1000000237"]}],"id":[{"id":"10.13039\/100012416","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-NS131554"],"award-info":[{"award-number":["R01-NS131554"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Transl. Stroke Res."],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experience poor outcomes despite successful recanalization, often due to severe neurological deterioration or life-threatening complications. Early identification of these high-risk patients remains a major unmet need. In this study, we developed and validated machine-learning (ML) models that integrate automated quantitative brain arterial morphology and collateral grading with demographic, clinical, laboratory, and imaging variables to predict major post-EVT complications and early neurological outcomes. Using a prospectively collected database of 727 AIS patients that underwent EVT, we developed ML models to incorporate patient-specific vascular morphometry with conventional clinical, laboratory, and imaging data to predict emergence of early neurological deterioration (END), symptomatic intracranial hemorrhage (sICH), malignant brain edema (MBE) requiring surgical decompression, and neurogenic respiratory failure and dysphagia requiring tracheostomy\/gastrostomy (TC\/PEG). Our analysis of morphological features, including increased tortuosity and reduced vessel diameter, showed strong associations with complications. Morphology-informed (MI) models consistently outperformed baseline-clinical (BC) models for patients with END (AUROC 0.81 for MI model vs. 0.73 for BC), sICH (AUROC 0.68 MI vs. 0.56 BC model), MBE (AUROC 0.67 MI model vs. 0.56 BC), or patients who underwent TC\/PEG (AUROC 0.66MI vs. 0.58 BC model). Statistical testing confirmed significant AUROC improvements for END, sICH and mRS (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05), Finally, patient-specific calibrated probability profiles enabled individualized, multidimensional risk stratification, revealing distinct complication-specific risk patterns across patients. These findings demonstrate that cerebrovascular structure\u2014an often overlooked yet physiologically fundamental determinant of ischemic injury and reperfusion dynamics\u2014provides significant predictive information that is not captured by standard clinical or visual imaging assessments. Automated vascular segmentation and collateral grading techniques enable rapid and objective integration of cerebrovascular metrics into prognostic models, offering a scalable tool for precision risk stratification, supporting earlier intervention, targeted monitoring, and improved post-EVT management.\n                  <\/jats:p>","DOI":"10.1007\/s12975-026-01465-2","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:17:46Z","timestamp":1783408666000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications"],"prefix":"10.1007","volume":"17","author":[{"given":"Aditi","family":"Deshpande","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laith R","family":"Altaweel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seajin","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zelalem","family":"Bahiru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahddeus J","family":"Leiphart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pouya","family":"Tahsili-Fahadan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaveh","family":"Laksari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"1465_CR1","first-page":"e6","volume":"127","author":"AS Go","year":"2013","unstructured":"Go AS, et al. Heart disease and stroke statistics\u20132013 update: a report from the American Heart Association. Circulation. 2013;127:e6\u2013245.","journal-title":"Circulation"},{"key":"1465_CR2","unstructured":"Guidelines for the early management of patients with acute ischemic stroke. : 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association\/American Stroke Association | Stroke. https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/STR.0000000000000211"},{"key":"1465_CR3","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1001\/jama.2016.13647","volume":"316","author":"JL Saver","year":"2016","unstructured":"Saver JL, et al. Time to Treatment With Endovascular Thrombectomy and Outcomes From Ischemic Stroke: A Meta-analysis. JAMA. 2016;316:1279\u201389.","journal-title":"JAMA"},{"key":"1465_CR4","doi-asserted-by":"publisher","first-page":"e790","DOI":"10.1212\/WNL.0000000000013210","volume":"98","author":"FC Ng","year":"2022","unstructured":"Ng FC, et al. Prevalence and Significance of Impaired Microvascular Tissue Reperfusion Despite Macrovascular Angiographic Reperfusion (No-Reflow). Neurology. 2022;98:e790\u2013801.","journal-title":"Neurology"},{"key":"1465_CR5","doi-asserted-by":"publisher","first-page":"e75719","DOI":"10.1371\/journal.pone.0075719","volume":"8","author":"MM Grube","year":"2013","unstructured":"Grube MM, et al. Influence of Acute Complications on Outcome 3 Months after Ischemic Stroke. PLoS ONE. 2013;8:e75719.","journal-title":"PLoS ONE"},{"key":"1465_CR6","doi-asserted-by":"publisher","first-page":"223","DOI":"10.3233\/NRE-161407","volume":"40","author":"B Janus-Laszuk","year":"2017","unstructured":"Janus-Laszuk B, Mirowska-Guzel D, Sarzynska-Dlugosz I, Czlonkowska A. Effect of medical complications on the after-stroke rehabilitation outcome. NeuroRehabilitation. 2017;40:223\u201332.","journal-title":"NeuroRehabilitation"},{"key":"1465_CR7","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A8909","author":"A Deshpande","year":"2025","unstructured":"Deshpande A, Laksari K, Tahsili-Fahadan P, Latour LL, Luby M. Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients. Am J Neuroradiol. 2025. https:\/\/doi.org\/10.3174\/ajnr.A8909.","journal-title":"Am J Neuroradiol"},{"key":"1465_CR8","doi-asserted-by":"publisher","first-page":"703258","DOI":"10.3389\/fneur.2021.703258","volume":"12","author":"JM Hong","year":"2021","unstructured":"Hong JM, Kim DS, Kim M. Hemorrhagic Transformation After Ischemic Stroke: Mechanisms and Management. Front Neurol. 2021;12:703258.","journal-title":"Front Neurol"},{"key":"1465_CR9","first-page":"e500","volume":"54","author":"A Dower","year":"2023","unstructured":"Dower A, et al. Surgical Decompression for Malignant Cerebral Edema After Ischemic Stroke. Cochrane Rev Stroke. 2023;54:e500\u20132.","journal-title":"Cochrane Rev Stroke"},{"key":"1465_CR10","doi-asserted-by":"publisher","first-page":"e023175","DOI":"10.1161\/JAHA.121.023175","volume":"11","author":"AK Bonkhoff","year":"2022","unstructured":"Bonkhoff AK, et al. Development and Validation of Prediction Models for Severe Complications After Acute Ischemic Stroke: A Study Based on the Stroke Registry of Northwestern Germany. J Am Heart Association. 2022;11:e023175.","journal-title":"J Am Heart Association"},{"key":"1465_CR11","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1159\/000065512","volume":"48","author":"C Weimar","year":"2002","unstructured":"Weimar C, et al. Complications following Acute Ischemic Stroke. Eur Neurol. 2002;48:133\u201340.","journal-title":"Eur Neurol"},{"key":"1465_CR12","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1186\/s13195-023-01289-4","volume":"15","author":"M Lee","year":"2023","unstructured":"Lee M, et al. Prediction of post-stroke cognitive impairment after acute ischemic stroke using machine learning. Alz Res Therapy. 2023;15:147.","journal-title":"Alz Res Therapy"},{"key":"1465_CR13","doi-asserted-by":"publisher","first-page":"117727191878693","DOI":"10.1177\/1177271918786931","volume":"13","author":"T Lees","year":"2018","unstructured":"Lees T, et al. Heart Rate Variability as a Biomarker for Predicting Stroke, Post-stroke Complications and Functionality. Biomark Insights. 2018;13:1177271918786931.","journal-title":"Biomark Insights"},{"key":"1465_CR14","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1161\/01.STR.31.2.448","volume":"31","author":"KC Johnston","year":"2000","unstructured":"Johnston KC, et al. A Predictive Risk Model for Outcomes of Ischemic Stroke. Stroke. 2000;31:448\u201355.","journal-title":"Stroke"},{"key":"1465_CR15","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1161\/STROKEAHA.113.003806","volume":"45","author":"D Strbian","year":"2014","unstructured":"Strbian D, et al. Symptomatic Intracranial Hemorrhage After Stroke Thrombolysis. Stroke. 2014;45:752\u20138.","journal-title":"Stroke"},{"key":"1465_CR16","doi-asserted-by":"publisher","first-page":"863","DOI":"10.3390\/jpm11090863","volume":"11","author":"J-M Choi","year":"2021","unstructured":"Choi J-M, et al. Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning. J Personalized Med. 2021;11:863.","journal-title":"J Personalized Med"},{"key":"1465_CR17","doi-asserted-by":"publisher","first-page":"256","DOI":"10.25259\/JNRP_435_2024","volume":"16","author":"R Padhi","year":"2025","unstructured":"Padhi R, et al. Collateral score as a prognostic marker in acute anterior circulation stroke: A multiphase computed tomography angiography analysis. JNRP. 2025;16:256\u201363.","journal-title":"JNRP"},{"key":"1465_CR18","doi-asserted-by":"publisher","first-page":"28182","DOI":"10.1038\/s41598-024-79608-4","volume":"14","author":"L Ma","year":"2024","unstructured":"Ma L, et al. Prognostic value of morphology and hemodynamics in moyamoya disease for long-term outcomes and disease progression. Sci Rep. 2024;14:28182.","journal-title":"Sci Rep"},{"key":"1465_CR19","doi-asserted-by":"crossref","unstructured":"Deshpande, A., Zhang, L. Q., Balu, R., Yahyavi-Firouz-Abadi, N., Badjatia, N., Laksari, K., & Tahsili-Fahadan, P. Cerebrovascular morphology: Insights into normal variations, aging effects and disease implications. J Cereb Blood Flow Metab. 2025;45(7):1249\u201364. https:\/\/journals.sagepub.com\/doi\/abs\/10.1177\/0271678X251328537.","DOI":"10.1177\/0271678X251328537"},{"key":"1465_CR20","doi-asserted-by":"publisher","first-page":"1217796","DOI":"10.3389\/fneur.2023.1217796","volume":"14","author":"A Deshpande","year":"2023","unstructured":"Deshpande A, et al. End to end stroke triage using cerebrovascular morphology and machine learning. Front Neurol. 2023;14:1217796.","journal-title":"Front Neurol"},{"key":"1465_CR21","doi-asserted-by":"publisher","first-page":"102573","DOI":"10.1016\/j.nicl.2021.102573","volume":"30","author":"A Deshpande","year":"2021","unstructured":"Deshpande A, et al. Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature. Neuroimage Clin. 2021;30:102573.","journal-title":"Neuroimage Clin"},{"key":"1465_CR22","unstructured":"Prabhakaran S et al. Guideline for the early management of patients with acute ischemic stroke: a guideline from the american heart association\/american stroke association. Stroke. 2026;0."},{"key":"1465_CR23","doi-asserted-by":"publisher","unstructured":"Cereda CW, Christensen S, Campbell BC, Mishra NK, Mlynash M, Levi C, Straka M, Wintermark M, Bammer R, Albers GW, Parsons MW, Lansberg MG. A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard. J Cereb Blood Flow Metab. 2016;(10):1780\u20139. https:\/\/journals.sagepub.com\/doi\/full\/https:\/\/doi.org\/10.1177\/0271678X15610586","DOI":"10.1177\/0271678X15610586"},{"key":"1465_CR24","doi-asserted-by":"crossref","unstructured":"Karamchandani, R. R., Helms, A. M., Satyanarayana, S., Yang, H., Clemente, J. D., Defilipp, G., Strong, D., Rhoten, J. B., & Asimos, A. W. (2023). Automated detection of intracranial large vessel occlusions using Viz. ai software: Experience in a large, integrated stroke network. Brain and behavior, 13(1), e2808.","DOI":"10.1002\/brb3.2808"},{"key":"1465_CR25","doi-asserted-by":"crossref","unstructured":"Deshpande, A., Wang, J., Bochenek, K. M., Oliverio, P., Altaweel, L., Yi, S., Bahiru, Z., Moro, D., Heinzelmann,G. K., Tahsili-Fahadan, P., & Laksari, K. (2026). Automated CTA-derived collateral grading and morphologic metrics for enhanced prediction of post-stroke outcomes. American Journal of Neuroradiology.","DOI":"10.3174\/ajnr.A9289"},{"key":"1465_CR26","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-030-50153-2_27","volume-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems","author":"JA Muschelli","year":"2020","unstructured":"Muschelli JA, Publicly Available H, Resolution. Unbiased CT Brain Template. In: Lesot M-J, et al. editors. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cham: Springer International Publishing; 2020. pp. 358\u201366. https:\/\/doi.org\/10.1007\/978-3-030-50153-2_27."},{"key":"1465_CR27","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1161\/01.STR.0000086465.41263.06","volume":"34","author":"DS Liebeskind","year":"2003","unstructured":"Liebeskind DS. Collateral circulation. Stroke. 2003;34:2279\u201384.","journal-title":"Stroke"},{"key":"1465_CR28","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1002\/ana.23546","volume":"71","author":"D Strbian","year":"2012","unstructured":"Strbian D, et al. Symptomatic intracranial hemorrhage after stroke thrombolysis: The SEDAN Score. Ann Neurol. 2012;71:634\u201341.","journal-title":"Ann Neurol"},{"key":"1465_CR29","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1212\/WNL.50.2.341","volume":"50","author":"T Heinsius","year":"1998","unstructured":"Heinsius T, Bogousslavsky J, Van Melle G. Large infarcts in the middle cerebral artery territory Etiology and outcome patterns. Neurology. 1998;50:341\u201350.","journal-title":"Neurology"},{"key":"1465_CR30","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1161\/01.STR.0000109772.64650.18","volume":"35","author":"R Gupta","year":"2004","unstructured":"Gupta R, Connolly ES, Mayer S, Elkind M. S. V. Hemicraniectomy for Massive Middle Cerebral Artery Territory Infarction. Stroke. 2004;35:539\u201343.","journal-title":"Stroke"},{"key":"1465_CR31","doi-asserted-by":"publisher","unstructured":"Wabl R, Williamson CA, Pandey AS, Rajajee V. Long-term and delayed functional recovery in patients with severe cerebrovascular and traumatic brain injury requiring tracheostomy. 2018. https:\/\/doi.org\/10.3171\/2018.2.JNS173247","DOI":"10.3171\/2018.2.JNS173247"},{"key":"1465_CR32","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s12028-018-0596-7","volume":"30","author":"K Alsherbini","year":"2019","unstructured":"Alsherbini K, et al. Predictors for Tracheostomy with External Validation of the Stroke-Related Early Tracheostomy Score (SETscore). Neurocrit Care. 2019;30:185\u201392.","journal-title":"Neurocrit Care"},{"key":"1465_CR33","unstructured":"Percutaneous Endoscopic Gastrostomy (PEG) for Enteral Nutrition in Patients with Stroke. | EBSCOhost. https:\/\/openurl.ebsco.com\/EPDB%3Agcd%3A5%3A12848373\/detailv2?sid=ebsco%3Aplink%3Ascholar\/id=ebsco%3Agcd%3A10779514\/crl=c\/link_origin=scholar.google.com."},{"key":"1465_CR34","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s10072-019-03896-2","volume":"40","author":"C Gandolfo","year":"2019","unstructured":"Gandolfo C, et al. The predictive dysphagia score (PreDyScore) in the short- and medium-term post-stroke: a putative tool in PEG indication. Neurol Sci. 2019;40:1619\u201326.","journal-title":"Neurol Sci"},{"key":"1465_CR35","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1136\/pgmj.2007.066118","volume":"84","author":"B Thanvi","year":"2008","unstructured":"Thanvi B, Treadwell S, Robinson T. Early neurological deterioration in acute ischaemic stroke: predictors, mechanisms and management. Postgrad Med J. 2008;84:412\u20137.","journal-title":"Postgrad Med J"},{"key":"1465_CR36","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/s12028-019-00844-y","volume":"32","author":"Y Cheng","year":"2020","unstructured":"Cheng Y, et al. External Validation and Modification of the EDEMA Score for Predicting Malignant Brain Edema After Acute Ischemic Stroke. Neurocrit Care. 2020;32:104\u201312.","journal-title":"Neurocrit Care"},{"key":"1465_CR37","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1007\/s12028-021-01380-4","volume":"36","author":"C Wang","year":"2022","unstructured":"Wang C, et al. Early Prediction of Malignant Edema After Successful Recanalization in Patients with Acute Ischemic Stroke. Neurocrit Care. 2022;36:822\u201330.","journal-title":"Neurocrit Care"},{"key":"1465_CR38","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1212\/WNL.0b013e318245d2a9","volume":"78","author":"D Strbian","year":"2012","unstructured":"Strbian D, et al. Predicting outcome of IV thrombolysis\u2013treated ischemic stroke patients. Neurology. 2012;78:427\u201332.","journal-title":"Neurology"},{"key":"1465_CR39","doi-asserted-by":"crossref","unstructured":"Kaur, Manmeet, et al. HIAT2 Predicts Poor Functional Outcome, Palliative Care Involvement, and In-hospital Mortality in tPA Treated and Untreated Ischemic Stroke Patients (P1. 188). Neurology 86.16_supplement (2016): P1-188.","DOI":"10.1212\/WNL.86.16_supplement.P1.188"},{"key":"1465_CR40","doi-asserted-by":"crossref","unstructured":"Ong CJ et al. Enhanced detection of edema in malignant anterior circulation stroke (EDEMA) score. Stroke. 2017;48:1969\u201372.","DOI":"10.1161\/STROKEAHA.117.016733"},{"key":"1465_CR41","doi-asserted-by":"publisher","first-page":"w14538","DOI":"10.4414\/smw.2017.14538","volume":"147","author":"S Jung","year":"2017","unstructured":"Jung S, et al. Relevance of the cerebral collateral circulation in ischaemic stroke: time is brain, but collaterals set the pace. Swiss Med Wkly. 2017;147:w14538.","journal-title":"Swiss Med Wkly"},{"key":"1465_CR42","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1161\/01.STR.30.7.1432","volume":"30","author":"M Kluytmans","year":"1999","unstructured":"Kluytmans M, et al. Cerebral hemodynamics in relation to patterns of collateral flow. Stroke. 1999;30:1432\u20139.","journal-title":"Stroke"},{"key":"1465_CR43","doi-asserted-by":"publisher","unstructured":"Deshpande A, et al. Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease. J Neuroimaging. 2022;32(5):956\u201367. https:\/\/doi.org\/10.1111\/jon.13023","DOI":"10.1111\/jon.13023"},{"key":"1465_CR44","doi-asserted-by":"publisher","first-page":"921","DOI":"10.3174\/ajnr.A8601","volume":"46","author":"DA Lakhani","year":"2025","unstructured":"Lakhani DA, et al. The Cortical Vein Opacification Score (COVES) Is Independently Associated with DSA ASITN Collateral Score. Am J Neuroradiol. 2025;46:921\u20138.","journal-title":"Am J Neuroradiol"},{"key":"1465_CR45","unstructured":"Prognostic Evaluation Based on Cortical Vein Score Difference. in Stroke | Stroke. https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/STROKEAHA.113.001231"},{"key":"1465_CR46","doi-asserted-by":"publisher","first-page":"3813","DOI":"10.3390\/jcm15103813","volume":"15","author":"G Busto","year":"2026","unstructured":"Busto G, et al. Deep Medullary Vein Asymmetry and Clinical Outcomes in Patients with Ischemic Stroke and Successful Endovascular Treatment. J Clin Med. 2026;15:3813.","journal-title":"J Clin Med"},{"key":"1465_CR47","doi-asserted-by":"publisher","first-page":"e0120801","DOI":"10.1371\/journal.pone.0120801","volume":"10","author":"J Mucke","year":"2015","unstructured":"Mucke J, et al. Asymmetry of Deep Medullary Veins on Susceptibility Weighted MRI in Patients with Acute MCA Stroke Is Associated with Poor Outcome. PLoS ONE. 2015;10:e0120801.","journal-title":"PLoS ONE"}],"container-title":["Translational Stroke Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12975-026-01465-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12975-026-01465-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12975-026-01465-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T07:18:13Z","timestamp":1787815093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12975-026-01465-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,7]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["1465"],"URL":"https:\/\/doi.org\/10.1007\/s12975-026-01465-2","relation":{},"ISSN":["1868-4483","1868-601X"],"issn-type":[{"value":"1868-4483","type":"print"},{"value":"1868-601X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,7]]},"assertion":[{"value":"24 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no conflicts of interest to disclose.","order":1,"name":"Ethics","label":"Disclosures","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"76"}}