{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T01:34:26Z","timestamp":1788053666213,"version":"build-2784847793"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U20A20358"],"award-info":[{"award-number":["U20A20358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Capital's Funds for Health Improvement and Research","award":["2020-1-2041"],"award-info":[{"award-number":["2020-1-2041"]}]},{"name":"Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences","award":["2019-I2M-5-029"],"award-info":[{"award-number":["2019-I2M-5-029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Med Res"],"DOI":"10.1186\/s40001-024-02056-3","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T03:41:13Z","timestamp":1728531673000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning"],"prefix":"10.1186","volume":"29","author":[{"given":"Lamia","family":"Mbarek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siding","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoming","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuesong","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"issue":"5","key":"2056_CR1","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1136\/svn-2021-001374","volume":"7","author":"YJ Wang","year":"2022","unstructured":"Wang YJ, Li ZX, Gu HQ, Zhai Y, Zhou Q, Jiang Y, et al. China stroke statistics: an update on the 2019 report from the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke Vasc Neurol. 2022;7(5):415\u201350.","journal-title":"Stroke Vasc Neurol"},{"issue":"3","key":"2056_CR2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1136\/svn-2020-000457","volume":"5","author":"YJ Wang","year":"2020","unstructured":"Wang YJ, Li ZX, Gu HQ, Zhai Y, Jiang Y, Zhao XQ, et al. China Stroke Statistics 2019: a Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke Vasc Neurol. 2020;5(3):211\u201339.","journal-title":"Stroke Vasc Neurol"},{"issue":"9","key":"2056_CR3","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/S1474-4422(18)30233-3","volume":"17","author":"MS Ekker","year":"2018","unstructured":"Ekker MS, Boot EM, Singhal AB, Tan KS, Debette S, Tuladhar AM, et al. Epidemiology, aetiology, and management of ischaemic stroke in young adults. Lancet Neurol. 2018;17(9):790\u2013801.","journal-title":"Lancet Neurol"},{"issue":"3","key":"2056_CR4","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1161\/STROKEAHA.119.024156","volume":"51","author":"MG George","year":"2020","unstructured":"George MG. Risk factors for ischemic stroke in younger adults: a focused update. Stroke. 2020;51(3):729\u201335.","journal-title":"Stroke"},{"issue":"2","key":"2056_CR5","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1161\/STROKEAHA.122.040524","volume":"54","author":"MS Ekker","year":"2023","unstructured":"Ekker MS, Verhoeven JI, Schellekens MMI, Boot EM, Van Alebeek ME, Brouwers PJAM, et al. Risk factors and causes of ischemic stroke in 1322 young adults. Stroke. 2023;54(2):439\u201347.","journal-title":"Stroke"},{"issue":"9","key":"2056_CR6","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1016\/S1474-4422(16)30073-4","volume":"15","author":"VL Feigin","year":"2016","unstructured":"Feigin VL, Roth GA, Naghavi M, Parmar P, Krishnamurthi R, Chugh S, et al. Global burden of stroke and risk factors in 188 countries, during 1990\u20132013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 2016;15(9):913\u201324.","journal-title":"Lancet Neurol"},{"issue":"10","key":"2056_CR7","doi-asserted-by":"publisher","first-page":"2836","DOI":"10.1161\/STROKEAHA.117.016904","volume":"48","author":"Y Xian","year":"2017","unstructured":"Xian Y, Thomas L, Liang L, Federspiel JJ, Webb LE, Bushnell CD, et al. Unexplained variation for hospitals\u2019 use of inpatient rehabilitation and skilled nursing facilities after an acute ischemic stroke. Stroke. 2017;48(10):2836\u201342.","journal-title":"Stroke"},{"issue":"1","key":"2056_CR8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1177\/2396987316629860","volume":"1","author":"J Putaala","year":"2016","unstructured":"Putaala J. Ischemic stroke in the young: current perspectives on incidence, risk factors, and cardiovascular prognosis. Eur Stroke J. 2016;1(1):28\u201340.","journal-title":"Eur Stroke J"},{"issue":"5","key":"2056_CR9","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1161\/STROKEAHA.118.024293","volume":"50","author":"J Heo","year":"2019","unstructured":"Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning-based model for prediction of outcomes in acute stroke. Stroke. 2019;50(5):1263\u20135.","journal-title":"Stroke"},{"issue":"8","key":"2056_CR10","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1080\/01616412.2019.1609159","volume":"41","author":"H Saber","year":"2019","unstructured":"Saber H, Somai M, Rajah GB, Scalzo F, Liebeskind DS. Predictive analytics and machine learning in stroke and neurovascular medicine. Neurol Res. 2019;41(8):681\u201390.","journal-title":"Neurol Res"},{"key":"2056_CR11","doi-asserted-by":"publisher","first-page":"kwz189","DOI":"10.1093\/aje\/kwz189","volume":"188","author":"Q Bi","year":"2019","unstructured":"Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A primer for the epidemiologist. Am J Epidemiol. 2019;188:kwz189.","journal-title":"Am J Epidemiol"},{"issue":"1","key":"2056_CR12","doi-asserted-by":"publisher","first-page":"10071","DOI":"10.1038\/s41598-021-89434-7","volume":"11","author":"C Fernandez-Lozano","year":"2021","unstructured":"Fernandez-Lozano C, Hervella P, Mato-Abad V, Rodr\u00edguez-Y\u00e1\u00f1ez M, Su\u00e1rez-Garaboa S, L\u00f3pez-Dequidt I, et al. Random forest-based prediction of stroke outcome. Sci Rep. 2021;11(1):10071.","journal-title":"Sci Rep"},{"issue":"6","key":"2056_CR13","doi-asserted-by":"publisher","first-page":"e0129569","DOI":"10.1371\/journal.pone.0129569","volume":"10","author":"ND Forkert","year":"2015","unstructured":"Forkert ND, Verleger T, Cheng B, Thomalla G, Hilgetag CC, Fiehler J. Multiclass support vector machine-based lesion mapping predicts functional outcome in ischemic stroke patients. Baron JC, editor. PLoS ONE. 2015;10(6):e0129569.","journal-title":"PLoS ONE"},{"issue":"1","key":"2056_CR14","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12874-022-01672-z","volume":"22","author":"SD Chen","year":"2022","unstructured":"Chen SD, You J, Yang XM, Gu HQ, Huang XY, Liu H, et al. Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke. BMC Med Res Methodol. 2022;22(1):195.","journal-title":"BMC Med Res Methodol"},{"issue":"5","key":"2056_CR15","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/diagnostics13050842","volume":"13","author":"CC Chung","year":"2023","unstructured":"Chung CC, Su ECY, Chen JH, Chen YT, Kuo CY. XGBoost-based simple three-item model accurately predicts outcomes of acute Ischemic stroke. Diagnostics. 2023;13(5):842.","journal-title":"Diagnostics"},{"issue":"12","key":"2056_CR16","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1038\/s41551-022-00898-y","volume":"6","author":"A Zhang","year":"2022","unstructured":"Zhang A, Xing L, Zou J, Wu JC. Shifting machine learning for healthcare from development to deployment and from models to data. Nat Biomed Eng. 2022;6(12):1330\u201345.","journal-title":"Nat Biomed Eng"},{"key":"2056_CR17","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining. San Francisco California USA: ACM; 2016. p. 785\u201394. https:\/\/doi.org\/10.1145\/2939672.2939785.Accessed 17 Oct 2023.","DOI":"10.1145\/2939672.2939785."},{"issue":"1","key":"2056_CR18","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1186\/s13059-021-02492-y","volume":"22","author":"J Yan","year":"2021","unstructured":"Yan J, Xu Y, Cheng Q, Jiang S, Wang Q, Xiao Y, et al. LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biol. 2021;22(1):271.","journal-title":"Genome Biol"},{"issue":"1","key":"2056_CR19","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3847\/1538-3881\/aaf101","volume":"157","author":"I Reis","year":"2019","unstructured":"Reis I, Baron D, Shahaf S. Probabilistic random forest: a machine learning algorithm for noisy data sets. Astron J. 2019;157(1):16.","journal-title":"Astron J"},{"issue":"1","key":"2056_CR20","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/ijerph18010159","volume":"18","author":"T Peng","year":"2020","unstructured":"Peng T, Chen X, Wan M, Jin L, Wang X, Du X, et al. The prediction of hepatitis E through ensemble learning. Int J Environ Res Public Health. 2020;18(1):159.","journal-title":"Int J Environ Res Public Health"},{"issue":"3","key":"2056_CR21","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1136\/svn-2019-000242","volume":"4","author":"Y Wang","year":"2019","unstructured":"Wang Y, Jing J, Meng X, Pan Y, Wang Y, Zhao X, et al. The Third China National Stroke Registry (CNSR-III) for patients with acute ischaemic stroke or transient ischaemic attack: design, rationale and baseline patient characteristics. Stroke Vasc Neurol. 2019;4(3):158\u201364.","journal-title":"Stroke Vasc Neurol."},{"issue":"6","key":"2056_CR22","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.3390\/ijerph17061828","volume":"17","author":"Y Wu","year":"2020","unstructured":"Wu Y, Fang Y. Stroke prediction with machine learning methods among older Chinese. Int J Environ Res Public Health. 2020;17(6):1828.","journal-title":"Int J Environ Res Public Health"},{"issue":"13","key":"2056_CR23","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1212\/WNL.0000000000000817","volume":"83","author":"NAMM Maaijwee","year":"2014","unstructured":"Maaijwee NAMM, Rutten-Jacobs LCA, Arntz RM, Schaapsmeerders P, Schoonderwaldt HC, Van Dijk EJ, et al. Long-term increased risk of unemployment after young stroke: a long-term follow-up study. Neurology. 2014;83(13):1132\u20138.","journal-title":"Neurology"},{"issue":"9","key":"2056_CR24","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.901240","volume":"6","author":"X Huang","year":"2022","unstructured":"Huang X, Cao T, Chen L, Li J, Tan Z, Xu B, et al. Novel insights on establishing machine learning-based stroke prediction models among hypertensive adults. Front Cardiovasc Med. 2022;6(9): 901240.","journal-title":"Front Cardiovasc Med"},{"issue":"9","key":"2056_CR25","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1161\/CIRCRESAHA.117.311312","volume":"121","author":"B Ambale-Venkatesh","year":"2017","unstructured":"Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res. 2017;121(9):1092\u2013101.","journal-title":"Circ Res"},{"issue":"11","key":"2056_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.539509","volume":"19","author":"X Li","year":"2020","unstructured":"Li X, Pan X, Jiang C, Wu M, Liu Y, Wang F, et al. Predicting 6-month unfavorable outcome of acute ischemic stroke using machine learning. Front Neurol. 2020;19(11): 539509.","journal-title":"Front Neurol"},{"issue":"14","key":"2056_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2022.897611","volume":"18","author":"X Zhang","year":"2022","unstructured":"Zhang X, Fei N, Zhang X, Wang Q, Fang Z. Machine learning prediction models for postoperative stroke in elderly patients: analyses of the MIMIC database. Front Aging Neurosci. 2022;18(14): 897611.","journal-title":"Front Aging Neurosci"},{"issue":"1","key":"2056_CR28","doi-asserted-by":"publisher","first-page":"3779","DOI":"10.1038\/s41598-020-60628-9","volume":"10","author":"JP Z\u00f6llner","year":"2020","unstructured":"Z\u00f6llner JP, Misselwitz B, Kaps M, Stein M, Konczalla J, Roth C, et al. National Institutes of Health Stroke Scale (NIHSS) on admission predicts acute symptomatic seizure risk in ischemic stroke: a population-based study involving 135,117 cases. Sci Rep. 2020;10(1):3779.","journal-title":"Sci Rep"},{"issue":"3","key":"2056_CR29","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1161\/01.STR.0000258355.23810.c6","volume":"38","author":"JL Banks","year":"2007","unstructured":"Banks JL, Marotta CA. Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis. Stroke. 2007;38(3):1091\u20136.","journal-title":"Stroke"},{"issue":"3","key":"2056_CR30","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1111\/j.1600-0404.2007.00822.x","volume":"116","author":"U Waje-Andreassen","year":"2007","unstructured":"Waje-Andreassen U, Naess H, Thomassen L, Eide GE, Vedeler CA. Long-term mortality among young ischemic stroke patients in western Norway. Acta Neurol Scand. 2007;116(3):150\u20136.","journal-title":"Acta Neurol Scand"},{"issue":"4","key":"2056_CR31","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1111\/ijs.12133","volume":"9","author":"A Lindmark","year":"2014","unstructured":"Lindmark A, Glader EL, Asplund K, Norrving B, Eriksson M. For the Riks-StrokeCollaboration. Socioeconomic disparities in stroke case fatality\u2013observations from riks-stroke, the Swedish stroke register. Int J Stroke. 2014;9(4):429\u201336.","journal-title":"Int J Stroke"},{"issue":"2","key":"2056_CR32","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1016\/j.jstrokecerebrovasdis.2014.09.024","volume":"24","author":"AW Aron","year":"2015","unstructured":"Aron AW, Staff I, Fortunato G, McCullough LD. Prestroke living situation and depression contribute to initial stroke severity and stroke recovery. J Stroke Cerebrovasc Dis. 2015;24(2):492\u20139.","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"10","key":"2056_CR33","doi-asserted-by":"publisher","first-page":"3083","DOI":"10.1161\/STROKEAHA.114.006520","volume":"45","author":"MJ Reeves","year":"2014","unstructured":"Reeves MJ, Prager M, Fang J, Stamplecoski M, Kapral MK. Impact of living alone on the care and outcomes of patients with acute stroke. Stroke. 2014;45(10):3083\u20135.","journal-title":"Stroke"},{"issue":"6","key":"2056_CR34","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1161\/STROKEAHA.109.577106","volume":"41","author":"M Eriksson","year":"2010","unstructured":"Eriksson M, Jonsson F, Appelros P, \u00c5sberg KH, Norrving B, Stegmayr B, et al. Dissemination of thrombolysis for acute ischemic stroke across a nation: experiences from the swedish stroke register, 2003 to 2008. Stroke. 2010;41(6):1115\u201322.","journal-title":"Stroke"},{"issue":"2","key":"2056_CR35","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1097\/YCO.0b013e3282f3ad89","volume":"21","author":"M Reblin","year":"2008","unstructured":"Reblin M, Uchino BN. Social and emotional support and its implication for health. Curr Opin Psychiatry. 2008;21(2):201\u20135.","journal-title":"Curr Opin Psychiatry"},{"issue":"12","key":"2056_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2019.104441","volume":"28","author":"Y Xu","year":"2019","unstructured":"Xu Y, Yang X, Huang H, Peng C, Ge Y, Wu H, et al. Extreme gradient boosting model has a better performance in predicting the risk of 90-Day readmissions in patients with ischaemic stroke. J Stroke Cerebrovasc Dis. 2019;28(12): 104441.","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"15","key":"2056_CR37","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.3390\/app10155135","volume":"10","author":"N Caball\u00e9-Cervig\u00f3n","year":"2020","unstructured":"Caball\u00e9-Cervig\u00f3n N, Castillo-Sequera JL, G\u00f3mez-Pulido JA, G\u00f3mez-Pulido JM, Polo-Luque ML. Machine learning applied to diagnosis of human diseases: a systematic review. Appl Sci. 2020;10(15):5135.","journal-title":"Appl Sci"},{"issue":"34","key":"2056_CR38","doi-asserted-by":"publisher","first-page":"E1351","DOI":"10.1503\/cmaj.202434","volume":"193","author":"AA Verma","year":"2021","unstructured":"Verma AA, Murray J, Greiner R, Cohen JP, Shojania KG, Ghassemi M, et al. Implementing machine learning in medicine. Can Med Assoc J. 2021;193(34):E1351\u20137.","journal-title":"Can Med Assoc J"}],"container-title":["European Journal of Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40001-024-02056-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40001-024-02056-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40001-024-02056-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T15:10:06Z","timestamp":1728573006000},"score":1,"resource":{"primary":{"URL":"https:\/\/eurjmedres.biomedcentral.com\/articles\/10.1186\/s40001-024-02056-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2056"],"URL":"https:\/\/doi.org\/10.1186\/s40001-024-02056-3","relation":{},"ISSN":["2047-783X"],"issn-type":[{"value":"2047-783X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"28 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The CNSR-III was approved by the Ethics Committee at Beijing Tiantan Hospital (IRB approval number: KY2015-001\u201301) and all participating centers. It was\u00a0conducted\u00a0following\u00a0the\u00a0Declaration\u00a0of\u00a0Helsinki\u00a0(2013\u00a0revision). All participants had informed consent from the patient or legally authorized representative (primarily spouse, parents, adult children, otherwise indicated).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publications"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"494"}}