{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:01:01Z","timestamp":1771473661761,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Guangzhou Higher Education Teaching Quality and Reform Project of Guangzhou Medical University","award":["2023YLKC021"],"award-info":[{"award-number":["2023YLKC021"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-02043-y","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T12:36:07Z","timestamp":1764074167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning"],"prefix":"10.1186","volume":"25","author":[{"given":"Yongxia","family":"Lei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chu Ann","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiping","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xilai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gengjia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifa","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinchun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"2043_CR1","doi-asserted-by":"crossref","unstructured":"Khan SR, Pearle MS, Robertson WG, Gambaro G, Canales BK, Doizi S, Traxer O, Tiselius H-G. Kidney stones. Nat Rev Dis Primers. 2016;2.","DOI":"10.1038\/nrdp.2016.8"},{"key":"2043_CR2","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1038\/s41581-020-0320-7","volume":"16","author":"C Thongprayoon","year":"2020","unstructured":"Thongprayoon C, Krambeck AE, Rule AD. Determining the true burden of kidney stone disease. Nat Rev Nephrol. 2020;16:736\u201346.","journal-title":"Nat Rev Nephrol"},{"key":"2043_CR3","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1007\/s00345-017-2008-6","volume":"35","author":"I Sorokin","year":"2017","unstructured":"Sorokin I, Mamoulakis C, Miyazawa K, Rodgers A, Talati J, Lotan Y. Epidemiology of stone disease across the world. World J Urol. 2017;35:1301\u201320.","journal-title":"World J Urol"},{"key":"2043_CR4","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.ajur.2018.08.007","volume":"5","author":"Y Liu","year":"2018","unstructured":"Liu Y, Chen Y, Liao B, Luo D, Wang K, Li H, Zeng G. Epidemiology of urolithiasis in Asia. Asian J Urol. 2018;5:205\u201314.","journal-title":"Asian J Urol"},{"key":"2043_CR5","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1681\/ASN.2013091011","volume":"25","author":"AD Rule","year":"2014","unstructured":"Rule AD, Lieske JC, Li X, Melton LJ, Krambeck AE, Bergstralh EJ. The ROKS nomogram for predicting a second symptomatic stone episode. J Am Soc Nephrol. 2014;25:2878\u201386.","journal-title":"J Am Soc Nephrol"},{"key":"2043_CR6","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1681\/ASN.2018121241","volume":"30","author":"MR D\u2019Costa","year":"2019","unstructured":"D\u2019Costa MR, Haley WE, Mara KC, Enders FT, Vrtiska TJ, Pais VM, Jacobsen SJ, McCollough CH, Lieske JC, Rule AD. Symptomatic and radiographic manifestations of kidney stone recurrence and their prediction by risk factors: A prospective cohort study. J Am Soc Nephrol. 2019;30:1251\u201360.","journal-title":"J Am Soc Nephrol"},{"key":"2043_CR7","doi-asserted-by":"crossref","unstructured":"Vaughan LE, Enders FT, Lieske JC, Pais VM, Rivera ME, Mehta RA, Vrtiska TJ, Rule AD. Predictors of symptomatic kidney stone recurrence after the first and subsequent episodes. Mayo Clin Proc. 2019;94:202\u201310.","DOI":"10.1016\/j.mayocp.2018.09.016"},{"key":"2043_CR8","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s00240-018-1043-0","volume":"46","author":"M Daudon","year":"2018","unstructured":"Daudon M, Jungers P, Bazin D, Williams JC. Recurrence rates of urinary calculi according to stone composition and morphology. Urolithiasis. 2018;46:459\u201370.","journal-title":"Urolithiasis"},{"key":"2043_CR9","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.urology.2020.04.067","volume":"143","author":"P Prasanchaimontri","year":"2020","unstructured":"Prasanchaimontri P, Monga M. Predictive factors for kidney stone recurrence in type 2 diabetes mellitus. Urology. 2020;143:85\u201390.","journal-title":"Urology"},{"key":"2043_CR10","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.purol.2022.02.010","volume":"32","author":"F Baowaidan","year":"2022","unstructured":"Baowaidan F, Zugail AS, Lyoubi Y, Culty T, Lebdai S, Brassart E, Bigot P. Incidence and risk factors for urolithiasis recurrence after endourological management of kidney stones: A retrospective single-centre study. Progr\u00e8s En Urologie. 2022;32:601\u20137.","journal-title":"Progr\u00e8s En Urologie"},{"key":"2043_CR11","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.juro.2014.05.006","volume":"192","author":"MS Pearle","year":"2014","unstructured":"Pearle MS, Goldfarb DS, Assimos DG, Curhan G, Denu-Ciocca CJ, Matlaga BR, Monga M, Penniston KL, Preminger GM, Turk TMT, White JR. Medical management of kidney stones: AUA guideline. J Urol. 2014;192:316\u201324.","journal-title":"J Urol"},{"key":"2043_CR12","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.eururo.2014.10.029","volume":"67","author":"A Skolarikos","year":"2015","unstructured":"Skolarikos A, Straub M, Knoll T, Sarica K, Seitz C, Pet\u0159\u00edk A, T\u00fcrk C. Metabolic evaluation and recurrence prevention for urinary stone patients: EAU guidelines. Eur Urol. 2015;67:750\u201363.","journal-title":"Eur Urol"},{"key":"2043_CR13","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1016\/j.juro.2016.10.052","volume":"197","author":"RS Hsi","year":"2017","unstructured":"Hsi RS, Sanford T, Goldfarb DS, Stoller ML. The role of the 24-Hour urine collection in the prevention of kidney stone recurrence. J Urol. 2017;197:1084\u20139.","journal-title":"J Urol"},{"key":"2043_CR14","doi-asserted-by":"crossref","unstructured":"Howles SA, Wiberg A, Goldsworthy M, Bayliss AL, Gluck AK, Ng M, Grout E, Tanikawa C, Kamatani Y, Terao C, Takahashi A, Kubo M, Matsuda K, Thakker RV, Turney BW, Furniss D. Genetic variants of calcium and vitamin D metabolism in kidney stone disease. Nat Commun. 2019;10.","DOI":"10.1038\/s41467-019-13145-x"},{"key":"2043_CR15","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1681\/ASN.2018090942","volume":"30","author":"C Tanikawa","year":"2019","unstructured":"Tanikawa C, Kamatani Y, Terao C, Usami M, Takahashi A, Momozawa Y, Suzuki K, Ogishima S, Shimizu A, Satoh M, Matsuo K, Mikami H, Naito M, Wakai K, Yamaji T, Sawada N, Iwasaki M, Tsugane S, Kohri K, Yu ASL, Yasui T, Murakami Y, Kubo M, Matsuda K. Novel risk loci identified in a Genome-Wide association study of urolithiasis in a Japanese population. J Am Soc Nephrol. 2019;30:855\u201364.","journal-title":"J Am Soc Nephrol"},{"key":"2043_CR16","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.1093\/ckj\/sfad119","volume":"16","author":"W Zhu","year":"2023","unstructured":"Zhu W, Zhang X, Zhou Z, Sun Y, Zhang G, Duan X, Huang Z, Ai G, Liu Y, Zhao Z, Zhong W, Zeng G. Predictive value of single-nucleotide polymorphism signature for nephrolithiasis recurrence: a 5-year prospective study. Clin Kidney J. 2023;16:2205\u201315.","journal-title":"Clin Kidney J"},{"key":"2043_CR17","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the Bridge between medical imaging and personalized medicine. Nat Reviews Clin Oncol. 2017;14:749\u201362.","journal-title":"Nat Reviews Clin Oncol"},{"key":"2043_CR18","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts HJWL. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441\u20136.","journal-title":"Eur J Cancer"},{"key":"2043_CR19","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1089\/end.2023.0263","volume":"38","author":"C Nedbal","year":"2024","unstructured":"Nedbal C, Cerrato C, Jahrreiss V, Pietropaolo A, Galosi AB, Castellani D, Somani BK. Trends of artificial Intelligence, machine Learning, virtual Reality, and radiomics in urolithiasis over the last 30 years (1994\u20132023) as published in the literature (PubMed): A comprehensive review. J Endourol. 2024;38:788\u201398.","journal-title":"J Endourol"},{"key":"2043_CR20","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.21037\/atm-21-965","volume":"9","author":"L Tang","year":"2021","unstructured":"Tang L, Li W, Zeng X, Wang R, Yang X, Luo G, Chen Q, Wang L, Song B. Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo. Annals Translational Med. 2021;9:1129.","journal-title":"Annals Translational Med"},{"key":"2043_CR21","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/j.kint.2021.05.031","volume":"100","author":"J Zheng","year":"2021","unstructured":"Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int. 2021;100:870\u201380.","journal-title":"Kidney Int"},{"key":"2043_CR22","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1097\/CM9.0000000000002866","volume":"137","author":"J Zheng","year":"2023","unstructured":"Zheng J, Zhang J, Cai J, Yao Y, Lu S, Wu Z, Cai Z, Tuerxun A, Batur J, Huang J, Kong J, Lin T. Development of a radiomics model to discriminate ammonium urate stones from uric acid stones in vivo: A remedy for the diagnostic pitfall of dual-energy computed tomography. Chin Med J. 2023;137:1095\u2013104.","journal-title":"Chin Med J"},{"key":"2043_CR23","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1007\/s00261-017-1309-y","volume":"43","author":"M Mannil","year":"2017","unstructured":"Mannil M, von Spiczak J, Hermanns T, Alkadhi H, Fankhauser CD. Prediction of successful shock wave lithotripsy with CT: a Phantom study using texture analysis. Abdom Radiol. 2017;43:1432\u20138.","journal-title":"Abdom Radiol"},{"key":"2043_CR24","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1089\/end.2017.0084","volume":"31","author":"HW Cui","year":"2017","unstructured":"Cui HW, Devlies W, Ravenscroft S, Heers H, Freidin AJ, Cleveland RO, Ganeshan B, Turney BW. CT texture analysis of ex vivo renal stones predicts ease of fragmentation with shockwave lithotripsy. J Endourol. 2017;31:694\u2013700.","journal-title":"J Endourol"},{"key":"2043_CR25","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1089\/end.2019.0475","volume":"34","author":"A Aminsharifi","year":"2020","unstructured":"Aminsharifi A, Irani D, Tayebi S, Jafari Kafash T, Shabanian T, Parsaei H. Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with guy\u2019s stone score and the CROES nomogram. J Endourol. 2020;34:692\u20139.","journal-title":"J Endourol"},{"key":"2043_CR26","doi-asserted-by":"publisher","first-page":"3734","DOI":"10.1007\/s00330-020-07498-x","volume":"31","author":"R Wang","year":"2020","unstructured":"Wang R, Su Y, Mao C, Li S, You M, Xiang S. Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success? Eur Radiol. 2020;31:3734\u201344.","journal-title":"Eur Radiol"},{"key":"2043_CR27","doi-asserted-by":"crossref","unstructured":"Zou XC, Luo CW, Yuan RM, Jin MN, Zeng T, Chao HC. Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy. Urolithiasis. 2024;52.","DOI":"10.1007\/s00240-024-01562-7"},{"key":"2043_CR28","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/s11604-022-01349-z","volume":"41","author":"P Kaviani","year":"2022","unstructured":"Kaviani P, Primak A, Bizzo B, Ebrahimian S, Saini S, Dreyer KJ, Kalra MK. Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT. Japanese J Radiol. 2022;41:194\u2013200.","journal-title":"Japanese J Radiol"},{"key":"2043_CR29","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1097\/MNH.0000000000000478","volume":"28","author":"MR D\u2019Costa","year":"2019","unstructured":"D\u2019Costa MR, Pais VM, Rule AD. Leave no stone unturned. Curr Opin Nephrol Hypertens. 2019;28:148\u201353.","journal-title":"Curr Opin Nephrol Hypertens"},{"key":"2043_CR30","doi-asserted-by":"crossref","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, G\u00f6tz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losneg\u00e5rd A, Maier-Hein KH, Morin O, M\u00fcller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, L\u00f6ck S. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328\u2013338.","DOI":"10.1148\/radiol.2020191145"},{"key":"2043_CR31","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1089\/end.2013.0161","volume":"27","author":"A Ciudin","year":"2013","unstructured":"Ciudin A, Luque MP, Salvador R, Diaconu MG, Franco A, Constantin V, Alvarez-Vijande R, Nicolau C, Alcaraz A. Abdominal computed Tomography\u2014A new tool for predicting recurrent stone disease. J Endourol. 2013;27:965\u20139.","journal-title":"J Endourol"},{"key":"2043_CR32","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.urology.2012.10.010","volume":"81","author":"A Ciudin","year":"2013","unstructured":"Ciudin A, Luque Galvez MP, Salvador Izquierdo R, Diaconu MG, Franco de Castro A, Constantin V, Alvarez-Vijande JR, Nicolau C. Alcaraz asensio A. Validation of randall\u2019s plaque theory using unenhanced abdominal computed tomography. Urology. 2013;81:246\u201350.","journal-title":"Urology"},{"key":"2043_CR33","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.urology.2014.08.031","volume":"85","author":"MG Selby","year":"2015","unstructured":"Selby MG, Vrtiska TJ, Krambeck AE, McCollough CH, Elsherbiny HE, Bergstralh EJ, Lieske JC, Rule AD. Quantification of asymptomatic kidney stone burden by computed tomography for predicting future symptomatic stone events. Urology. 2015;85:45\u201350.","journal-title":"Urology"},{"key":"2043_CR34","doi-asserted-by":"crossref","unstructured":"Zhu B, Nie Y, Zheng S, Lin S, Li Z, Wu W. CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones. Urolithiasis. 2024;52.","DOI":"10.1007\/s00240-024-01593-0"},{"key":"2043_CR35","doi-asserted-by":"crossref","unstructured":"Wang K, Ge J, Han W, Wang D, Zhao Y, Shen Y, Chen J, Chen D, Wu J, Shen N, Zhu S, Xue B, Xu X. Risk factors for kidney stone disease recurrence: a comprehensive meta-analysis. BMC Urol. 2022;22.","DOI":"10.1186\/s12894-022-01017-4"},{"key":"2043_CR36","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1111\/bju.13605","volume":"119","author":"V Ganesan","year":"2016","unstructured":"Ganesan V, De S, Greene D, Torricelli FCM, Monga M. Accuracy of ultrasonography for renal stone detection and size determination: is it good enough for management decisions? BJU Int. 2016;119:464\u20139.","journal-title":"BJU Int"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02043-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-02043-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02043-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T12:36:11Z","timestamp":1764074171000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-02043-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,25]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2043"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-02043-y","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,25]]},"assertion":[{"value":"5 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (ethical batch number: ES-2025-K071-01). Because of the retrospective nature of the study, the requirement for informed consent was waived with the approval of Medical Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University. All methods were carried out in accordance with relevant guidelines and regulations.","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 to publish"}},{"value":"declaration.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"489"}}