{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T01:45:09Z","timestamp":1794361509951,"version":"4.9.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"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":["Neurosurg Rev"],"DOI":"10.1007\/s10143-025-03773-x","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:33:46Z","timestamp":1756686826000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The efficacy of machine learning algorithms in evaluating factors associated with shunt-dependent hydrocephalus after subarachnoid hemorrhage: a systematic review and meta-analysis"],"prefix":"10.1007","volume":"48","author":[{"given":"Parisa","family":"Javadnia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nila","family":"Salimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bita","family":"Shokri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yousef","family":"Ramazani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Moradinazar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neda","family":"Khaledian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsan","family":"Alimohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"issue":"8","key":"3773_CR1","doi-asserted-by":"crossref","first-page":"e519","DOI":"10.1161\/STROKEAHA.110.581975","volume":"41","author":"T Al-Khindi","year":"2010","unstructured":"Al-Khindi T, Macdonald RL, Schweizer TA (2010) Cognitive and functional outcome after aneurysmal subarachnoid hemorrhage. Stroke 41(8):e519\u2013e36","journal-title":"Stroke"},{"issue":"10","key":"3773_CR2","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1161\/STROKEAHA.116.013739","volume":"47","author":"H Adams","year":"2016","unstructured":"Adams H, Ban VS, Leinonen V, Aoun SG, Huttunen J, Saavalainen T et al (2016) Risk of shunting after aneurysmal subarachnoid hemorrhage: a collaborative study and initiation of a consortium. Stroke 47(10):2488\u20132496","journal-title":"Stroke"},{"issue":"3","key":"3773_CR3","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1093\/neuros\/nyx393","volume":"83","author":"GM Paisan","year":"2018","unstructured":"Paisan GM, Ding D, Starke RM, Crowley RW, Liu KC (2018) Shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage: predictors and long-term functional outcomes. Neurosurgery 83(3):393\u2013402","journal-title":"Neurosurgery"},{"key":"3773_CR4","volume":"124","author":"H-J Mijderwijk","year":"2019","unstructured":"Mijderwijk H-J, Fischer I, Zhivotovskaya A, Bostelmann R, Steiger H-J, Cornelius JF, Petridis AK (2019) Prognostic model for chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. World Neurosurg 124:e572\u2013e9","journal-title":"World Neurosurg"},{"key":"3773_CR5","doi-asserted-by":"crossref","DOI":"10.1016\/j.wneu.2018.09.159","volume":"121","author":"M Lenski","year":"2019","unstructured":"Lenski M, Biczok A, Huge V, Forbrig R, Briegel J, Tonn J-C, Thon N (2019) Role of cerebrospinal fluid markers for predicting shunt-dependent hydrocephalus in patients with subarachnoid hemorrhage and external ventricular drain placement. World Neurosurg 121:e535\u2013e42","journal-title":"World Neurosurg"},{"issue":"7","key":"3773_CR6","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1161\/STR.0000000000000069","volume":"46","author":"JC Hemphill 3rd","year":"2015","unstructured":"Hemphill JC 3rd, Greenberg SM, Anderson CS, Becker K, Bendok BR, Cushman M et al (2015) Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association\/American Stroke Association. Stroke 46(7):2032\u20132060","journal-title":"Stroke"},{"issue":"3","key":"3773_CR7","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3340\/jkns.2014.56.3.175","volume":"56","author":"JE Kim","year":"2014","unstructured":"Kim JE, Ko SB, Kang HS, Seo DH, Park SQ, Sheen SH et al (2014) Clinical practice guidelines for the medical and surgical management of primary intracerebral hemorrhage in Korea. J Korean Neurosurg Soc 56(3):175\u2013187","journal-title":"J Korean Neurosurg Soc"},{"key":"3773_CR8","doi-asserted-by":"crossref","unstructured":"Dorai Z, Hynan LS, Kopitnik TA, Samson D (2003) Factors related to hydrocephalus after aneurysmal subarachnoid hemorrhage. Neurosurgery 52(4):763\u20139; discussion 9\u201371","DOI":"10.1227\/01.NEU.0000053222.74852.2D"},{"issue":"2","key":"3773_CR9","first-page":"120","volume":"21","author":"S Wolf","year":"2015","unstructured":"Wolf S (2015) Rationale for lumbar drains in aneurysmal subarachnoid hemorrhage. Curr Opin Crit Care 21(2):120\u2013126","journal-title":"Curr Opin Crit Care"},{"issue":"3","key":"3773_CR10","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1161\/STROKEAHA.108.191395","volume":"40","author":"JB Bederson","year":"2009","unstructured":"Bederson JB, Connolly ES Jr., Batjer HH, Dacey RG, Dion JE, Diringer MN et al (2009) Guidelines for the management of aneurysmal subarachnoid hemorrhage. Stroke 40(3):994\u20131025","journal-title":"Stroke"},{"issue":"5","key":"3773_CR11","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.3171\/2017.5.JNS163271","volume":"129","author":"EA Winkler","year":"2017","unstructured":"Winkler EA, Burkhardt J-K, Rutledge WC, Rick JW, Partow CP, Yue JK et al (2017) Reduction of shunt dependency rates following aneurysmal subarachnoid hemorrhage by tandem fenestration of the lamina terminalis and membrane of Liliequist during microsurgical aneurysm repair. J Neurosurg 129(5):1166\u20131172","journal-title":"J Neurosurg"},{"key":"3773_CR12","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.wneu.2017.06.119","volume":"106","author":"Z Xie","year":"2017","unstructured":"Xie Z, Hu X, Zan X, Lin S, Li H, You C (2017) Predictors of shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage? A systematic review and meta-analysis. World Neurosurg 106:844\u2013860 e6","journal-title":"World Neurosurg"},{"issue":"2","key":"3773_CR13","first-page":"181","volume":"64","author":"P Di Russo","year":"2019","unstructured":"Di Russo P, Di Carlo DT, Lutenberg A, Morganti R, Evins AI, Perrini P (2019) Shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. J Neurosurg Sci 64(2):181\u2013189","journal-title":"J Neurosurg Sci"},{"issue":"6","key":"3773_CR14","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3340\/jkns.2018.0152","volume":"62","author":"JH Kim","year":"2019","unstructured":"Kim JH, Kim JH, Kang HI, Kim DR, Moon BG, Kim JS (2019) Risk factors and preoperative risk scoring system for shunt-dependent hydrocephalus following aneurysmal subarachnoid hemorrhage. J Korean Neurosurg Soc 62(6):643\u2013648","journal-title":"J Korean Neurosurg Soc"},{"key":"3773_CR15","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s00701-018-3560-6","volume":"160","author":"YK Park","year":"2018","unstructured":"Park YK, Yi H-J, Choi K-S, Lee Y-J, Chun H-J, Kwon SM, Kim D-W (2018) Predicting factors for shunt-dependent hydrocephalus in patients with aneurysmal subarachnoid hemorrhage. Acta Neurochir 160:1407\u20131413","journal-title":"Acta Neurochir"},{"key":"3773_CR16","doi-asserted-by":"crossref","first-page":"e731","DOI":"10.1016\/j.wneu.2018.09.207","volume":"121","author":"J Zeng","year":"2019","unstructured":"Zeng J, Qin L, Wang D, Gong J, Pan J, Zhu Y et al (2019) Comparing the risk of shunt-dependent hydrocephalus in patients with ruptured intracranial aneurysms treated by endovascular coiling or surgical clipping: an updated meta-analysis. World Neurosurg 121:e731\u2013e8","journal-title":"World Neurosurg"},{"issue":"2","key":"3773_CR17","first-page":"226","volume":"28","author":"TS Jeong","year":"2018","unstructured":"Jeong TS, Yoo CJ, Kim WK, Yee GT, Kim EY, Kim MJ (2018) Factors related to the development of shunt-dependent hydrocephalus following subarachnoid hemorrhage in the elderly. Turk Neurosurg 28(2):226\u2013233","journal-title":"Turk Neurosurg"},{"issue":"1","key":"3773_CR18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s00062-024-01474-4","volume":"35","author":"K Daga","year":"2025","unstructured":"Daga K, Agarwal S, Moti Z, Lee MBK, Din M, Wood D et al (2025) Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review. Clin Neuroradiol 35(1):3\u201316","journal-title":"Clin Neuroradiol"},{"issue":"5","key":"3773_CR19","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.3171\/2016.12.JNS162400","volume":"128","author":"D Diesing","year":"2017","unstructured":"Diesing D, Wolf S, Sommerfeld J, Sarrafzadeh A, Vajkoczy P, Dengler NF (2017) A novel score to predict shunt dependency after aneurysmal subarachnoid hemorrhage. J Neurosurg 128(5):1273\u20131279","journal-title":"J Neurosurg"},{"issue":"11","key":"3773_CR20","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1007\/s00701-014-2200-z","volume":"156","author":"HO Erixon","year":"2014","unstructured":"Erixon HO, Sorteberg A, Sorteberg W, Eide PK (2014) Predictors of shunt dependency after aneurysmal subarachnoid hemorrhage: results of a single-center clinical trial. Acta Neurochir (Wien) 156(11):2059\u20132069","journal-title":"Acta Neurochir (Wien)"},{"key":"3773_CR21","doi-asserted-by":"crossref","first-page":"1366306","DOI":"10.3389\/fneur.2024.1366306","volume":"15","author":"D Rao","year":"2024","unstructured":"Rao D, Yang L, Enxi X, Siyuan L, Yu Q, Zheng L et al (2024) A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study. Front Neurol 15:1366306","journal-title":"Front Neurol"},{"key":"3773_CR22","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1227\/NEU.0000000000000677","volume":"76","author":"HA Zaidi","year":"2015","unstructured":"Zaidi HA, Montoure A, Elhadi A, Nakaji P, McDougall CG, Albuquerque FC et al (2015) Long-term functional outcomes and predictors of shunt-dependent hydrocephalus after treatment of ruptured intracranial aneurysms in the BRAT trial. Neurosurgery 76:608\u2013615","journal-title":"Neurosurgery"},{"issue":"8","key":"3773_CR23","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.jocn.2012.09.033","volume":"20","author":"L Lai","year":"2013","unstructured":"Lai L, Morgan MK (2013) Predictors of in-hospital shunt-dependent hydrocephalus following rupture of cerebral aneurysms. J Clin Neurosci 20(8):1134\u20131138","journal-title":"J Clin Neurosci"},{"issue":"1","key":"3773_CR24","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s12028-019-00886-2","volume":"33","author":"A Perry","year":"2020","unstructured":"Perry A, Graffeo CS, Kleinstern G, Carlstrom LP, Link MJ, Rabinstein AA (2020) Quantitative modeling of external ventricular drain output to predict shunt dependency in aneurysmal subarachnoid hemorrhage: cohort study. Neurocrit Care 33(1):218\u2013229","journal-title":"Neurocrit Care"},{"issue":"12","key":"3773_CR25","volume":"12","author":"MK Na","year":"2017","unstructured":"Na MK, Won YD, Kim CH, Kim JM, Cheong JH, Ryu JI, Han MH (2017) Early variations of laboratory parameters predicting shunt-dependent hydrocephalus after subarachnoid hemorrhage. PLoS One 12(12):e0189499","journal-title":"PLoS One"},{"issue":"3","key":"3773_CR26","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1227\/NEU.0b013e31828048ce","volume":"72","author":"C Muroi","year":"2013","unstructured":"Muroi C, Hugelshofer M, Seule M, Tastan I, Fujioka M, Mishima K, Keller E (2013) Correlation among systemic inflammatory parameter, occurrence of delayed neurological deficits, and outcome after aneurysmal subarachnoid hemorrhage. Neurosurgery 72(3):367\u2013375","journal-title":"Neurosurgery"},{"issue":"5","key":"3773_CR27","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.3171\/2021.5.JNS211033","volume":"136","author":"FC Robertson","year":"2022","unstructured":"Robertson FC, Sha RM, Amich JM, Ibn Essayed W, Lal A, Lee BH et al (2022) Frameless neuronavigation with computer vision and real-time tracking for bedside external ventricular drain placement: a cadaveric study. J Neurosurg 136(5):1475\u20131484","journal-title":"J Neurosurg"},{"issue":"14","key":"3773_CR28","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380(14):1347\u20131358","journal-title":"N Engl J Med"},{"issue":"13","key":"3773_CR29","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","volume":"319","author":"AL Beam","year":"2018","unstructured":"Beam AL, Kohane IS (2018) Big data and machine learning in health care. JAMA 319(13):1317\u20131318","journal-title":"JAMA"},{"issue":"12","key":"3773_CR30","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1007\/s00701-020-04484-6","volume":"162","author":"G Muscas","year":"2020","unstructured":"Muscas G, Matteuzzi T, Becattini E, Orlandini S, Battista F, Laiso A et al (2020) Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. Acta Neurochir 162(12):3093\u20133105","journal-title":"Acta Neurochir"},{"issue":"4","key":"3773_CR31","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.neurad.2022.11.001","volume":"50","author":"B Joo","year":"2023","unstructured":"Joo B, Ahn SS, An C, Han K, Choi D, Kim H et al (2023) Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: glioblastoma, lymphoma, and metastasis. J Neuroradiol 50(4):388\u2013395","journal-title":"J Neuroradiol"},{"issue":"1","key":"3773_CR32","doi-asserted-by":"crossref","first-page":"22641","DOI":"10.1038\/s41598-023-50012-8","volume":"13","author":"N Stroh","year":"2023","unstructured":"Stroh N, Stefanits H, Maletzky A, Kaltenleithner S, Thumfart S, Giretzlehner M et al (2023) Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms. Sci Rep 13(1):22641","journal-title":"Sci Rep"},{"issue":"1","key":"3773_CR33","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s10143-023-02114-0","volume":"46","author":"D Frey","year":"2023","unstructured":"Frey D, Hilbert A, Fr\u00fch A, Madai VI, Kossen T, Kiewitz J et al (2023) Enhancing the prediction for shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach. Neurosurg Rev 46(1):206","journal-title":"Neurosurg Rev"},{"issue":"14","key":"3773_CR34","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.3390\/diagnostics13142429","volume":"13","author":"JA Tangsrivimol","year":"2023","unstructured":"Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, H\u00e4rtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C (2023) Artificial intelligence in neurosurgery: a state-of-the-art review from past to future. Diagnostics. 13(14):2429","journal-title":"Diagnostics"},{"issue":"4","key":"3773_CR35","doi-asserted-by":"crossref","first-page":"e0301541","DOI":"10.1371\/journal.pone.0301541","volume":"19","author":"S Uddin","year":"2024","unstructured":"Uddin S, Lu H (2024) Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data. PLoS One 19(4):e0301541","journal-title":"PLoS One"},{"key":"3773_CR36","doi-asserted-by":"crossref","unstructured":"Miles J (2005) Tolerance and variance inflation factor. Encyclopedia of statistics in behavioral science","DOI":"10.1002\/0470013192.bsa683"},{"issue":"5","key":"3773_CR37","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206\u2013215","journal-title":"Nat Mach Intell"},{"key":"3773_CR38","doi-asserted-by":"crossref","unstructured":"Lakkaraju H, Bach SH, Leskovec J (eds) (2016) Interpretable decision sets: A joint framework for description and prediction. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2939672.2939874"},{"key":"3773_CR39","doi-asserted-by":"crossref","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, p 372","DOI":"10.1136\/bmj.n71"},{"issue":"1","key":"3773_CR40","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS et al (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170(1):51\u201358","journal-title":"Ann Intern Med"},{"issue":"1","key":"3773_CR41","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29\u201336","journal-title":"Radiology"},{"issue":"1","key":"3773_CR42","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s10143-024-03175-5","volume":"48","author":"I Mohammadzadeh","year":"2025","unstructured":"Mohammadzadeh I, Niroomand B, Eini P, Khaledian H, Choubineh T, Luzzi S (2025) Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants. Neurosurg Rev 48(1):26","journal-title":"Neurosurg Rev"},{"issue":"3","key":"3773_CR43","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s12028-023-01832-z","volume":"40","author":"LS Santana","year":"2024","unstructured":"Santana LS, Diniz JBC, Rabelo NN, Teixeira MJ, Figueiredo EG, Telles JPM (2024) Machine learning algorithms to predict delayed cerebral ischemia after subarachnoid hemorrhage: a systematic review and meta-analysis. Neurocrit Care 40(3):1171\u20131181","journal-title":"Neurocrit Care"},{"key":"3773_CR44","doi-asserted-by":"crossref","unstructured":"Hajikarimloo B, Mohammadzadeh I, Habibi MA, Tos SM, Asgarzadeh A, Tajvidi M et al (2025) Machine learning models in the prediction of chronic or shunt-dependent hydrocephalus following subarachnoid hemorrhage: a systematic review and meta-analysis. Neuroradiol J 19714009251345104","DOI":"10.1177\/19714009251345104"},{"issue":"5","key":"3773_CR45","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1007\/s10877-024-01151-4","volume":"38","author":"N Schweingruber","year":"2024","unstructured":"Schweingruber N, Bremer J, Wiehe A, Mader MM, Mayer C, Woo MS et al (2024) Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data. J Clin Monit Comput 38(5):1175\u20131186","journal-title":"J Clin Monit Comput"},{"key":"3773_CR46","doi-asserted-by":"crossref","first-page":"E124","DOI":"10.1016\/j.wneu.2024.09.047","volume":"192","author":"M Gollwitzer","year":"2024","unstructured":"Gollwitzer M, Steindl M, Stroh N, Hauser A, Sardi G, Rossmann T et al (2024) Machine learningebased prediction of chronic Shunt-Dependent hydrocephalus after spontaneous subarachnoid hemorrhage. World Neurosurg 192:E124\u2013E33","journal-title":"World Neurosurg"},{"issue":"6","key":"3773_CR47","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.3171\/2017.7.JNS17677","volume":"129","author":"IC Hostettler","year":"2018","unstructured":"Hostettler IC, Muroi C, Richter JK, Schmid J, Neidert MC, Seule M et al (2018) Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis. J Neurosurg 129(6):1499\u20131510","journal-title":"J Neurosurg"}],"container-title":["Neurosurgical Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10143-025-03773-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10143-025-03773-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10143-025-03773-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T04:15:55Z","timestamp":1772079355000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10143-025-03773-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3773"],"URL":"https:\/\/doi.org\/10.1007\/s10143-025-03773-x","relation":{},"ISSN":["1437-2320"],"issn-type":[{"value":"1437-2320","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"2 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","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"}},{"value":"Clinical trial number: not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"629"}}