{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:17:41Z","timestamp":1784740661940,"version":"3.55.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China","award":["LBY23H200008"],"award-info":[{"award-number":["LBY23H200008"]}]},{"name":"the Medical Health Science and Technology Project of Zhejiang Provincial","award":["2023RC272"],"award-info":[{"award-number":["2023RC272"]}]},{"name":"the Medical Health Science and Technology Project of Zhejiang Provincial","award":["2022KY1207"],"award-info":[{"award-number":["2022KY1207"]}]},{"name":"the Science and Technology Planning Project of Wenzhou","award":["Y2023088"],"award-info":[{"award-number":["Y2023088"]}]},{"name":"the Science and Technology Planning Project of Wenzhou","award":["ZY2021025"],"award-info":[{"award-number":["ZY2021025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03254-7","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:48:48Z","timestamp":1762778928000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting the risk of preterm birth with machine learning and electronic health records in China"],"prefix":"10.1186","volume":"25","author":[{"given":"Lushuai","family":"Qian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanyue","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"issue":"9606","key":"3254_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/s0140-6736(08)60074-4","volume":"371","author":"RL Goldenberg","year":"2008","unstructured":"Goldenberg RL, Culhane JF, Iams JD, et al. Epidemiology and causes of preterm birth. Lancet. 2008;371(9606):75\u201384. https:\/\/doi.org\/10.1016\/s0140-6736(08)60074-4.","journal-title":"Lancet"},{"key":"3254_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.bpobgyn.2018.04.003","volume":"52","author":"JP Vogel","year":"2018","unstructured":"Vogel JP, Chawanpaiboon S, Moller A-B, et al. The global epidemiology of preterm birth. Best Pract Res Clin Obstet Gynecol. 2018;52:3\u201312. https:\/\/doi.org\/10.1016\/j.bpobgyn.2018.04.003.","journal-title":"Best Pract Res Clin Obstet Gynecol"},{"issue":"1","key":"3254_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1002\/ijgo.13195","volume":"150","author":"SR Walani","year":"2020","unstructured":"Walani SR. Global burden of preterm birth. Int J Gynecol Obstet. 2020;150(1):31\u20133. https:\/\/doi.org\/10.1002\/ijgo.13195.","journal-title":"Int J Gynecol Obstet"},{"key":"3254_CR4","doi-asserted-by":"publisher","unstructured":"Ohuma EO, Moller AB, Bradley E et al. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis. Lancet,2023,402(10409): 1261\u201371. https:\/\/doi.org\/10.1016\/s0140-6736(23)00878-4","DOI":"10.1016\/s0140-6736(23)00878-4"},{"key":"3254_CR5","doi-asserted-by":"publisher","unstructured":"Blencowe H, Cousens S, Chou D, et al. Born too soon: the global epidemiology of 15 million preterm births. Reprod Health. 2013;10(1). https:\/\/doi.org\/10.1186\/1742-4755-10-s1-s2. S2.","DOI":"10.1186\/1742-4755-10-s1-s2"},{"key":"3254_CR6","doi-asserted-by":"publisher","DOI":"10.17226\/11622","volume-title":"Preterm birth: Causes, Consequences, and prevention","author":"B Premature","year":"2007","unstructured":"Institute of Medicine Committee on Understanding, Premature B, Assuring Healthy O. Preterm birth: Causes, Consequences, and prevention. Washington (DC): National Academy of Sciences; 2007. https:\/\/doi.org\/10.17226\/11622."},{"key":"3254_CR7","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.bpobgyn.2018.05.002","volume":"52","author":"Y Ville","year":"2018","unstructured":"Ville Y, Rozenberg P. Predictors of preterm birth. Best Pract Res Clin Obstet Gynecol. 2018;52:23\u201332. https:\/\/doi.org\/10.1016\/j.bpobgyn.2018.05.002.","journal-title":"Best Pract Res Clin Obstet Gynecol"},{"issue":"3","key":"3254_CR8","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1097\/AOG.0b013e318199924a","volume":"113","author":"DM Haas","year":"2009","unstructured":"Haas DM, Imperiale TF, Kirkpatrick PR, et al. Tocolytic therapy: a meta-analysis and decision analysis. Obstet Gynecol. 2009;113(3):585\u201394. https:\/\/doi.org\/10.1097\/AOG.0b013e318199924a.","journal-title":"Obstet Gynecol"},{"issue":"2","key":"3254_CR9","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.ejogrb.2009.10.027","volume":"148","author":"N Thomakos","year":"2010","unstructured":"Thomakos N, Daskalakis G, Papapanagiotou A, et al. Amniotic fluid interleukin-6 and tumor necrosis factor-\u03b1 at mid-trimester genetic amniocentesis: relationship to intra-amniotic microbial invasion and preterm delivery. Eur J Obstet Gynecol Reproductive Biology. 2010;148(2):147\u201351. https:\/\/doi.org\/10.1016\/j.ejogrb.2009.10.027.","journal-title":"Eur J Obstet Gynecol Reproductive Biology"},{"key":"3254_CR10","doi-asserted-by":"publisher","unstructured":"Chakoory O, Barra V, Rochette E, et al. DeepMPTB: a vaginal microbiome-based deep neural network as artificial intelligence strategy for efficient preterm birth prediction. Biomark Res. 2024;12(1). https:\/\/doi.org\/10.1186\/s40364-024-00557-1.","DOI":"10.1186\/s40364-024-00557-1"},{"key":"3254_CR11","doi-asserted-by":"publisher","unstructured":"Hashemi L, Shahshahan Z. Maternal serum cytokines in the prediction of preterm labor and response to tocolytic therapy in preterm labor women. Adv Biomedical Res. 2014;3(1). https:\/\/doi.org\/10.4103\/2277-9175.133243.","DOI":"10.4103\/2277-9175.133243"},{"issue":"5","key":"3254_CR12","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1111\/aji.12502","volume":"75","author":"CN Cordeiro","year":"2016","unstructured":"Cordeiro CN, Savva Y, Vaidya D, et al. Mathematical modeling of the biomarker milieu to characterize preterm birth and predict adverse neonatal outcomes. Am J Reprod Immunol. 2016;75(5):594\u2013601. https:\/\/doi.org\/10.1111\/aji.12502.","journal-title":"Am J Reprod Immunol"},{"issue":"1","key":"3254_CR13","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.ajog.2022.04.002","volume":"227","author":"J Camunas-Soler","year":"2022","unstructured":"Camunas-Soler J, Gee EPS, Reddy M, et al. Predictive RNA profiles for early and very early spontaneous preterm birth. Am J Obstet Gynecol. 2022;227(1):72e71-72.e16. https:\/\/doi.org\/10.1016\/j.ajog.2022.04.002.","journal-title":"Am J Obstet Gynecol"},{"key":"3254_CR14","doi-asserted-by":"publisher","unstructured":"Considine EC, Khashan AS, Kenny LC. Screening for preterm birth: potential for a metabolomics biomarker panel. Metabolites. 2019;9(5). https:\/\/doi.org\/10.3390\/metabo9050090.","DOI":"10.3390\/metabo9050090"},{"issue":"Suppl 1","key":"3254_CR15","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1111\/j.1471-0528.2005.00583.x","volume":"112","author":"F Goffinet","year":"2005","unstructured":"Goffinet F. Primary predictors of preterm labour. BJOG. 2005;112(Suppl 1):38\u201347. https:\/\/doi.org\/10.1111\/j.1471-0528.2005.00583.x.","journal-title":"BJOG"},{"issue":"1","key":"3254_CR16","doi-asserted-by":"publisher","first-page":"5683","DOI":"10.1038\/s41598-025-89905-1","volume":"15","author":"A Kloska","year":"2025","unstructured":"Kloska A, Harmoza A, Kloska SM, et al. Predicting preterm birth using machine learning methods. Sci Rep. 2025;15(1):5683. https:\/\/doi.org\/10.1038\/s41598-025-89905-1.","journal-title":"Sci Rep"},{"issue":"1","key":"3254_CR17","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.cell.2019.02.039","volume":"177","author":"NS Abul-Husn","year":"2019","unstructured":"Abul-Husn NS, Kenny EE. Personalized medicine and the power of electronic health records. Cell. 2019;177(1):58\u201369. https:\/\/doi.org\/10.1016\/j.cell.2019.02.039.","journal-title":"Cell"},{"key":"3254_CR18","doi-asserted-by":"publisher","unstructured":"Li Y, Fu X, Guo X, et al. Maternal preterm birth prediction in the united states: a case-control database study. BMC Pediatr. 2022;22(1). https:\/\/doi.org\/10.1186\/s12887-022-03591-w.","DOI":"10.1186\/s12887-022-03591-w"},{"key":"3254_CR19","doi-asserted-by":"publisher","unstructured":"Abraham A, Le B, Kosti I, et al. Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth. BMC Med. 2022;20(1). https:\/\/doi.org\/10.1186\/s12916-022-02522-x.","DOI":"10.1186\/s12916-022-02522-x"},{"key":"3254_CR20","doi-asserted-by":"publisher","unstructured":"Li Y, Lou Y, Liu M, et al. Machine learning based biomarker discovery for chronic kidney disease\u2013mineral and bone disorder (CKD-MBD). BMC Med Inf Decis Mak. 2024;24(1). https:\/\/doi.org\/10.1186\/s12911-024-02421-6.","DOI":"10.1186\/s12911-024-02421-6"},{"issue":"2","key":"3254_CR21","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jiixd.2024.01.002","volume":"3","author":"A Mumuni","year":"2025","unstructured":"Mumuni A, Mumuni F. Automated data processing and feature engineering for deep learning and big data applications: A survey. J Inform Intell. 2025;3(2):113\u201353. https:\/\/doi.org\/10.1016\/j.jiixd.2024.01.002.","journal-title":"J Inform Intell"},{"issue":"1","key":"3254_CR22","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/tnnls.2020.2975837","volume":"32","author":"H Xue","year":"2021","unstructured":"Xue H, Huynh DQ, Reynolds M. PoPPL: pedestrian trajectory prediction by LSTM with automatic route class clustering. IEEE Trans Neural Netw Learn Syst. 2021;32(1):77\u201390. https:\/\/doi.org\/10.1109\/tnnls.2020.2975837.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"3254_CR23","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1007\/s10930-021-10003-y","volume":"40","author":"VA Jisna","year":"2021","unstructured":"Jisna VA, Jayaraj PB. Protein structure prediction: conventional and deep learning perspectives. Protein J. 2021;40(4):522\u201344. https:\/\/doi.org\/10.1007\/s10930-021-10003-y.","journal-title":"Protein J"},{"key":"3254_CR24","doi-asserted-by":"publisher","first-page":"100325","DOI":"10.1016\/j.smhl.2022.100325","volume":"26","author":"IK Nti","year":"2022","unstructured":"Nti IK, Owusu-Boadu B. A hybrid boosting ensemble model for predicting maternal mortality and sustaining reproductive. Smart Health. 2022;26:100325. https:\/\/doi.org\/10.1016\/j.smhl.2022.100325.","journal-title":"Smart Health"},{"key":"3254_CR25","doi-asserted-by":"publisher","unstructured":"World Health O. Born too soon: the global action report on preterm birth. Geneva; World Health Organization.https:\/\/doi.org\/10.1186\/1742-4755-10-s1-s1","DOI":"10.1186\/1742-4755-10-s1-s1"},{"key":"3254_CR26","doi-asserted-by":"publisher","unstructured":"Zhang Y, Du S, Hu T, et al. Establishment of a model for predicting preterm birth based on the machine learning algorithm. BMC Pregnancy Childbirth. 2023;23(1). https:\/\/doi.org\/10.1186\/s12884-023-06058-7.","DOI":"10.1186\/s12884-023-06058-7"},{"key":"3254_CR27","doi-asserted-by":"publisher","unstructured":"Szecsi PB, Arabi Belaghi R, Beyene J, et al. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS ONE. 2021;16(6). https:\/\/doi.org\/10.1371\/journal.pone.0252025.","DOI":"10.1371\/journal.pone.0252025"},{"key":"3254_CR28","doi-asserted-by":"publisher","unstructured":"Begum M, Redoy RM, Das Anty A. Preterm Baby Birth Prediction using Machine Learning Techniques [Z]. 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD). 2021: 50\u201354.https:\/\/doi.org\/10.1109\/icict4sd50815.2021.9396933","DOI":"10.1109\/icict4sd50815.2021.9396933"},{"key":"3254_CR29","doi-asserted-by":"publisher","unstructured":"Gao C, Osmundson S, Velez Edwards DR, et al. Deep learning predicts extreme preterm birth from electronic health records. J Biomed Inform. 2019;100. https:\/\/doi.org\/10.1016\/j.jbi.2019.103334.","DOI":"10.1016\/j.jbi.2019.103334"},{"key":"3254_CR30","doi-asserted-by":"publisher","unstructured":"Hershey M, Burris HH, Cereceda D, et al. Predicting the risk of spontaneous premature births using clinical data and machine learning. Inf Med Unlocked. 2022;32. https:\/\/doi.org\/10.1016\/j.imu.2022.101053.","DOI":"10.1016\/j.imu.2022.101053"},{"issue":"11","key":"3254_CR31","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1111\/aogs.13895","volume":"99","author":"P Kuusela","year":"2020","unstructured":"Kuusela P, Wennerholm UB, Fadl H, et al. Second trimester cervical length measurements with transvaginal ultrasound: A prospective observational agreement and reliability study. Acta Obstet Gynecol Scand. 2020;99(11):1476\u201385. https:\/\/doi.org\/10.1111\/aogs.13895.","journal-title":"Acta Obstet Gynecol Scand"},{"issue":"6","key":"3254_CR32","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1007\/s00404-020-05872-0","volume":"303","author":"J Zhang","year":"2021","unstructured":"Zhang J, Pan M, Zhan W, et al. Two-stage nomogram models in mid-gestation for predicting the risk of spontaneous preterm birth in twin pregnancy. Arch Gynecol Obstet. 2021;303(6):1439\u201349. https:\/\/doi.org\/10.1007\/s00404-020-05872-0.","journal-title":"Arch Gynecol Obstet"},{"issue":"2","key":"3254_CR33","doi-asserted-by":"publisher","first-page":"102287","DOI":"10.1016\/j.jogoh.2021.102287","volume":"51","author":"P Guerby","year":"2022","unstructured":"Guerby P, Fillion A, Pasquier JC, et al. Evaluation of midtrimester cervical length thresholds for the prediction of spontaneous preterm birth. J Gynecol Obstet Hum Reprod. 2022;51(2):102287. https:\/\/doi.org\/10.1016\/j.jogoh.2021.102287.","journal-title":"J Gynecol Obstet Hum Reprod"},{"issue":"1","key":"3254_CR34","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1186\/s12884-024-06980-4","volume":"24","author":"L Ding","year":"2024","unstructured":"Ding L, Yin X, Wen G, et al. Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China. BMC Pregnancy Childbirth. 2024;24(1):810. https:\/\/doi.org\/10.1186\/s12884-024-06980-4.","journal-title":"BMC Pregnancy Childbirth"},{"issue":"11","key":"3254_CR35","doi-asserted-by":"publisher","first-page":"6500","DOI":"10.62347\/tnwa5229","volume":"16","author":"Y Xiu","year":"2024","unstructured":"Xiu Y, Lin Z, Pan M. Development and validation of a risk prediction model for spontaneous preterm birth. Am J Transl Res. 2024;16(11):6500\u20139. https:\/\/doi.org\/10.62347\/tnwa5229.","journal-title":"Am J Transl Res"},{"issue":"1","key":"3254_CR36","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1186\/s12884-024-06822-3","volume":"24","author":"X Huang","year":"2024","unstructured":"Huang X, Zhou Y, Liu B, et al. Prediction model for spontaneous preterm birth less than 32 weeks of gestation in low-risk women with mid-trimester short cervical length: a retrospective cohort study. BMC Pregnancy Childbirth. 2024;24(1):621. https:\/\/doi.org\/10.1186\/s12884-024-06822-3.","journal-title":"BMC Pregnancy Childbirth"},{"key":"3254_CR37","doi-asserted-by":"publisher","unstructured":"Bitar G, Liu W, Tunguhan J, et al. A machine learning algorithm using clinical and demographic data for all-cause preterm birth prediction. Am J Perinatol. 2024;41:01. https:\/\/doi.org\/10.1055\/s-0043-1776917","DOI":"10.1055\/s-0043-1776917"},{"issue":"3","key":"3254_CR38","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1002\/ijgo.15514","volume":"166","author":"A Ramachandran","year":"2024","unstructured":"Ramachandran A, Clottey KD, Gordon A, et al. Prediction and prevention of preterm birth: quality assessment and systematic review of clinical practice guidelines using the AGREE II framework. Int J Gynaecol Obstet. 2024;166(3):932\u201342. https:\/\/doi.org\/10.1002\/ijgo.15514.","journal-title":"Int J Gynaecol Obstet"},{"issue":"2","key":"3254_CR39","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1038\/s41390-021-01633-0","volume":"91","author":"VG Jain","year":"2022","unstructured":"Jain VG, Willis KA, Jobe A, et al. Chorioamnionitis and neonatal outcomes. Pediatr Res. 2022;91(2):289\u201396. https:\/\/doi.org\/10.1038\/s41390-021-01633-0.","journal-title":"Pediatr Res"},{"issue":"3","key":"3254_CR40","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1002\/jcu.23354","volume":"51","author":"M Impis Oglou","year":"2023","unstructured":"Impis Oglou M, Tsakiridis I, Mamopoulos A, et al. Cervical length screening for predicting preterm birth: A comparative review of guidelines. J Clin Ultrasound. 2023;51(3):472\u20138. https:\/\/doi.org\/10.1002\/jcu.23354.","journal-title":"J Clin Ultrasound"},{"key":"3254_CR41","doi-asserted-by":"publisher","unstructured":"Hu J, Szymczak S. A review on longitudinal data analysis with random forest. Brief Bioinform. 2023;24(2). https:\/\/doi.org\/10.1093\/bib\/bbad002.","DOI":"10.1093\/bib\/bbad002"},{"key":"3254_CR42","doi-asserted-by":"publisher","unstructured":"Ishwaran H, Kogalur UB, Blackstone EH, et al. Random survival forests. Annals Appl Stat. 2008;2(3). https:\/\/doi.org\/10.1214\/08-aoas169.","DOI":"10.1214\/08-aoas169"},{"issue":"6","key":"3254_CR43","doi-asserted-by":"publisher","first-page":"2056","DOI":"10.1038\/s41390-024-03604-7","volume":"97","author":"CH Shu","year":"2025","unstructured":"Shu CH, Zebda R, Espinosa C, et al. Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning. Pediatr Res. 2025;97(6):2056\u201364. https:\/\/doi.org\/10.1038\/s41390-024-03604-7.","journal-title":"Pediatr Res"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03254-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03254-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03254-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T18:07:55Z","timestamp":1762884475000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03254-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3254"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03254-7","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"6 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 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":"The study was approved by the ethics committee of Wenzhou People\u2019s Hospital (ethics number: WRY2021-215) in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All subjects provided informed written consent.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"415"}}