{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T02:40:20Z","timestamp":1789958420719,"version":"4.0.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2\u201381.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0\u201380.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.<\/jats:p>","DOI":"10.1038\/s41598-021-97043-7","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T10:03:02Z","timestamp":1630404182000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Machine learning-based approach for disease severity classification of carpal tunnel syndrome"],"prefix":"10.1038","volume":"11","author":[{"given":"Dougho","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byung Hee","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Eok","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Young","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mansu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heum Dai","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mun-Chul","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ae Ryoung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyoung Seop","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang Woo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"97043_CR1","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.rbo.2013.08.007","volume":"49","author":"M Chammas","year":"2014","unstructured":"Chammas, M. et al. Carpal tunnel syndrome\u2014Part I (anatomy, physiology, etiology and diagnosis). Rev. Bras. Ortop. 49, 429\u2013436 (2014).","journal-title":"Rev. Bras. Ortop."},{"key":"97043_CR2","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1001\/jama.282.2.153","volume":"282","author":"I Atroshi","year":"1999","unstructured":"Atroshi, I. et al. Prevalence of carpal tunnel syndrome in a general population. JAMA 282, 153\u2013158 (1999).","journal-title":"JAMA"},{"key":"97043_CR3","first-page":"952","volume":"83","author":"KE LeBlanc","year":"2011","unstructured":"LeBlanc, K. E. & Cestia, W. Carpal tunnel syndrome. Am. Fam. Physician 83, 952\u2013958 (2011).","journal-title":"Am. Fam. Physician"},{"key":"97043_CR4","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1002\/mus.22208","volume":"44","author":"RA Werner","year":"2011","unstructured":"Werner, R. A. & Andary, M. Electrodiagnostic evaluation of carpal tunnel syndrome. Muscle Nerve 44, 597\u2013607 (2011).","journal-title":"Muscle Nerve"},{"key":"97043_CR5","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1212\/WNL.58.11.1589","volume":"58","author":"CK Jablecki","year":"2002","unstructured":"Jablecki, C. K. et al. Practice parameter: Electrodiagnostic studies in carpal tunnel syndrome. Report of the American Association of Electrodiagnostic Medicine, American Academy of Neurology, and the American Academy of Physical Medicine and Rehabilitation. Neurology 58, 1589\u20131592 (2002).","journal-title":"Neurology"},{"key":"97043_CR6","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1002\/1097-4598(200008)23:8<1280::AID-MUS20>3.0.CO;2-Y","volume":"23","author":"JD Bland","year":"2000","unstructured":"Bland, J. D. A neurophysiological grading scale for carpal tunnel syndrome. Muscle Nerve 23, 1280\u20131283 (2000).","journal-title":"Muscle Nerve"},{"key":"97043_CR7","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1002\/(SICI)1097-4598(199712)20:12<1477::AID-MUS1>3.0.CO;2-5","volume":"20","author":"JC Stevens","year":"1997","unstructured":"Stevens, J. C. AAEM minimonograph #26: The electrodiagnosis of carpal tunnel syndrome. American Association of Electrodiagnostic Medicine. Muscle Nerve 20, 1477\u20131486 (1997).","journal-title":"Muscle Nerve"},{"key":"97043_CR8","doi-asserted-by":"publisher","first-page":"254","DOI":"10.5535\/arm.2013.37.2.254","volume":"37","author":"HJ Lee","year":"2013","unstructured":"Lee, H. J., Kwon, H. K., Kim, D. H. & Pyun, S. B. Nerve conduction studies of median motor nerve and median sensory branches according to the severity of carpal tunnel syndrome. Ann. Rehabil. Med. 37, 254\u2013262 (2013).","journal-title":"Ann. Rehabil. Med."},{"key":"97043_CR9","unstructured":"Rosario, N. B. & De Jesus, O. Electrodiagnostic evaluation of carpal tunnel syndrome. In StatPearls (StatPearls Publishing, Treasure Island (FL), 2021)."},{"key":"97043_CR10","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1002\/mus.25421","volume":"55","author":"ZN London","year":"2017","unstructured":"London, Z. N. Safety and pain in electrodiagnostic studies. Muscle Nerve 55, 149\u2013159 (2017).","journal-title":"Muscle Nerve"},{"key":"97043_CR11","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s11019-021-10008-5","volume":"24","author":"G Starke","year":"2021","unstructured":"Starke, G., De Clercq, E. & Elger, B. S. Towards a pragmatist dealing with algorithmic bias in medical machine learning. Med. Health Care Philos. 24, 341\u2013349 (2021).","journal-title":"Med. Health Care Philos."},{"key":"97043_CR12","doi-asserted-by":"publisher","first-page":"643","DOI":"10.14245\/ns.1938386.193","volume":"16","author":"JT Schwartz","year":"2019","unstructured":"Schwartz, J. T. et al. Applications of machine learning using electronic medical records in spine surgery. Neurospine 16, 643\u2013653 (2019).","journal-title":"Neurospine"},{"key":"97043_CR13","doi-asserted-by":"publisher","first-page":"e1001381","DOI":"10.1371\/journal.pmed.1001381","volume":"10","author":"EW Steyerberg","year":"2013","unstructured":"Steyerberg, E. W. et al. Prognosis Research Strategy (PROGRESS) 3: Prognostic model research. PLoS Med 10, e1001381 (2013).","journal-title":"PLoS Med"},{"key":"97043_CR14","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1038\/s41746-019-0148-3","volume":"2","author":"P Shah","year":"2019","unstructured":"Shah, P. et al. Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digit. Med. 2, 69 (2019).","journal-title":"NPJ Digit. Med."},{"key":"97043_CR15","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.patrec.2020.02.020","volume":"133","author":"AA Ardakani","year":"2020","unstructured":"Ardakani, A. A. et al. Diagnosis of carpal tunnel syndrome: A comparative study of shear wave elastography, morphometry and artificial intelligence techniques. Pattern Recogn. Lett. 133, 77\u201385 (2020).","journal-title":"Pattern Recogn. Lett."},{"key":"97043_CR16","first-page":"993","volume":"94","author":"J Wipperman","year":"2016","unstructured":"Wipperman, J. & Goerl, K. Carpal tunnel syndrome: Diagnosis and management. Am. Fam. Physician 94, 993\u2013999 (2016).","journal-title":"Am. Fam. Physician"},{"key":"97043_CR17","first-page":"32","volume":"8","author":"S Eslami","year":"2019","unstructured":"Eslami, S., Fadaei, B., Baniasadi, M. & Yavari, P. Clinical presentation of carpal tunnel syndrome with different severity: A cross sectional study. Am. J. Clin. Exp. Immunol. 8, 32\u201336 (2019).","journal-title":"Am. J. Clin. Exp. Immunol."},{"key":"97043_CR18","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1186\/s12891-019-2928-y","volume":"20","author":"S Hirani","year":"2019","unstructured":"Hirani, S. A study to further develop and refine carpal tunnel syndrome (CTS) nerve conduction grading tool. BMC Musculoskelet. Disord. 20, 581 (2019).","journal-title":"BMC Musculoskelet. Disord."},{"key":"97043_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1111\/j.1600-0404.1997.tb00271.x","volume":"96","author":"L Padua","year":"1997","unstructured":"Padua, L. et al. Neurophysiological classification and sensitivity in 500 carpal tunnel syndrome hands. Acta Neurol. Scand. 96, 211\u2013217 (1997).","journal-title":"Acta Neurol. Scand."},{"key":"97043_CR20","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1007\/s00415-012-6497-3","volume":"259","author":"NM Kane","year":"2012","unstructured":"Kane, N. M. & Oware, A. Nerve conduction and electromyography studies. J. Neurol. 259, 1502\u20131508 (2012).","journal-title":"J. Neurol."},{"key":"97043_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s11420-005-0124-0","volume":"2","author":"J Feinberg","year":"2006","unstructured":"Feinberg, J. EMG: Myths and facts. HSS J. 2, 19\u201321 (2006).","journal-title":"HSS J."},{"key":"97043_CR22","doi-asserted-by":"publisher","first-page":"109518","DOI":"10.1016\/j.ejrad.2020.109518","volume":"136","author":"F Faeghi","year":"2021","unstructured":"Faeghi, F. et al. Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists\u2019 assessment. Eur. J. Radiol. 136, 109518 (2021).","journal-title":"Eur. J. Radiol."},{"key":"97043_CR23","first-page":"1654","volume":"67","author":"R Sayin","year":"2017","unstructured":"Sayin, R., Keskin, S. & Hamamci, M. Evaluation of several classification methods in carpal tunnel syndrome. J Pak Med Assoc 67, 1654\u20131657 (2017).","journal-title":"J Pak Med Assoc"},{"key":"97043_CR24","doi-asserted-by":"crossref","unstructured":"Wei, Y., Zhang, W. & Gu, F., Towards Diagnosis of Carpal Tunnel Syndrome Using Machine Learning, in 2020 3rd Artificial Intelligence and Cloud Computing Conference 76\u201382 (2020).","DOI":"10.1145\/3442536.3442549"},{"key":"97043_CR25","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1002\/bjs.11461","volume":"107","author":"SA Rahman","year":"2020","unstructured":"Rahman, S. A. et al. Machine learning to predict early recurrence after oesophageal cancer surgery. Br. J. Surg. 107, 1042\u20131052 (2020).","journal-title":"Br. J. Surg."},{"key":"97043_CR26","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3390\/diagnostics9040178","volume":"9","author":"W Chang","year":"2019","unstructured":"Chang, W. et al. A machine-learning-based prediction method for hypertension outcomes based on medical data. Diagnostics 9, 178 (2019).","journal-title":"Diagnostics"},{"key":"97043_CR27","doi-asserted-by":"publisher","first-page":"104441","DOI":"10.1016\/j.jstrokecerebrovasdis.2019.104441","volume":"28","author":"Y Xu","year":"2019","unstructured":"Xu, Y. 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. 28, 104441 (2019).","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"97043_CR28","doi-asserted-by":"publisher","first-page":"713","DOI":"10.3390\/ma14040713","volume":"14","author":"Z Wan","year":"2021","unstructured":"Wan, Z., Xu, Y. & Savija, B. On the use of machine learning models for prediction of compressive strength of concrete: Influence of dimensionality reduction on the model performance. Materials 14, 713 (2021).","journal-title":"Materials"},{"key":"97043_CR29","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neuroimage.2012.09.065","volume":"65","author":"KR Gray","year":"2013","unstructured":"Gray, K. R. et al. Random forest-based similarity measures for multi-modal classification of Alzheimer\u2019s disease. Neuroimage 65, 167\u2013175 (2013).","journal-title":"Neuroimage"},{"key":"97043_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"key":"97043_CR31","doi-asserted-by":"publisher","first-page":"332","DOI":"10.3390\/info11060332","volume":"11","author":"EK Ampomah","year":"2020","unstructured":"Ampomah, E. K., Qin, Z. & Nyame, G. Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information 11, 332 (2020).","journal-title":"Information"},{"key":"97043_CR32","doi-asserted-by":"publisher","first-page":"3488","DOI":"10.3390\/app10103488","volume":"10","author":"MA Cazares-Manr\u00edquez","year":"2020","unstructured":"Cazares-Manr\u00edquez, M. A. et al. A review of carpal tunnel syndrome and its association with age, body mass index, cardiovascular risk factors, hand dominance, and sex. Appl. Sci. 10, 3488 (2020).","journal-title":"Appl. Sci."},{"key":"97043_CR33","doi-asserted-by":"publisher","first-page":"86","DOI":"10.30574\/wjarr.2020.6.3.0182","volume":"6","author":"N Aziz","year":"2020","unstructured":"Aziz, N. Diagnosis of carpal tunnel syndrome in perspective of clinical features, neurophysiological studies and high resolution ultrasound. World J. Adv. Res. Rev. 6, 86\u201396 (2020).","journal-title":"World J. Adv. Res. Rev."},{"key":"97043_CR34","doi-asserted-by":"publisher","first-page":"455","DOI":"10.2217\/pmt-2018-0045","volume":"8","author":"A Oteo-Alvaro","year":"2018","unstructured":"Oteo-Alvaro, A. & Marin, M. T. Predictive factors of the neuropathic pain in patients with carpal tunnel syndrome and its impact on patient activity. Pain Manag. 8, 455\u2013463 (2018).","journal-title":"Pain Manag."},{"key":"97043_CR35","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3340\/jkns.2014.55.2.78","volume":"55","author":"MK Kim","year":"2014","unstructured":"Kim, M. K., Jeon, H. J., Park, S. H., Park, D. S. & Nam, H. S. Value of ultrasonography in the diagnosis of carpal tunnel syndrome: Correlation with electrophysiological abnormalities and clinical severity. J. Korean Neurosurg. Soc. 55, 78\u201382 (2014).","journal-title":"J. Korean Neurosurg. Soc."},{"key":"97043_CR36","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s00296-009-1061-x","volume":"30","author":"YS Karadag","year":"2010","unstructured":"Karadag, Y. S. et al. Severity of Carpal tunnel syndrome assessed with high frequency ultrasonography. Rheumatol. Int. 30, 761\u2013765 (2010).","journal-title":"Rheumatol. Int."},{"key":"97043_CR37","doi-asserted-by":"publisher","first-page":"W181","DOI":"10.2214\/AJR.11.7012","volume":"198","author":"A Mohammadi","year":"2012","unstructured":"Mohammadi, A., Ghasemi-Rad, M., Mladkova-Suchy, N. & Ansari, S. Correlation between the severity of carpal tunnel syndrome and color Doppler sonography findings. AJR Am. J. Roentgenol. 198, W181-184 (2012).","journal-title":"AJR Am. J. Roentgenol."},{"key":"97043_CR38","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1097\/PHM.0000000000001104","volume":"98","author":"P Roomizadeh","year":"2019","unstructured":"Roomizadeh, P. et al. Ultrasonographic assessment of carpal tunnel syndrome severity: A systematic review and meta-analysis. Am. J. Phys. Med. Rehabil. 98, 373\u2013381 (2019).","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"97043_CR39","unstructured":"Siriseriwan, W. smotefamily: a collection of oversampling techniques for class imbalance problem based on SMOTE. R package version 1.3.1. (2019)."},{"key":"97043_CR40","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/1471-2105-14-106","volume":"14","author":"R Blagus","year":"2013","unstructured":"Blagus, R. & Lusa, L. SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 14, 106 (2013).","journal-title":"BMC Bioinform."},{"key":"97043_CR41","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10654-018-0390-z","volume":"33","author":"AI Naimi","year":"2018","unstructured":"Naimi, A. I. & Balzer, L. B. Stacked generalization: An introduction to super learning. Eur. J. Epidemiol. 33, 459\u2013464 (2018).","journal-title":"Eur. J. Epidemiol."},{"key":"97043_CR42","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/TVCG.2020.3030352","volume":"27","author":"A Chatzimparmpas","year":"2021","unstructured":"Chatzimparmpas, A., Martins, R. M., Kucher, K. & Kerren, A. StackGenVis: Alignment of data, algorithms, and models for stacking ensemble learning using performance metrics. IEEE Trans. Vis. Comput. Graph 27, 1547\u20131557 (2021).","journal-title":"IEEE Trans. Vis. Comput. Graph"},{"key":"97043_CR43","doi-asserted-by":"publisher","first-page":"e0226910","DOI":"10.1371\/journal.pone.0226910","volume":"14","author":"M Wang","year":"2019","unstructured":"Wang, M. et al. A novel model for malaria prediction based on ensemble algorithms. PLoS ONE 14, e0226910 (2019).","journal-title":"PLoS ONE"},{"key":"97043_CR44","unstructured":"Zachary, D. M. & Jared, E. K. caretEnsemble: Ensembles of Caret Models. R package version 2.0.1. (2019)."},{"key":"97043_CR45","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1016\/j.apmr.2013.05.008","volume":"94","author":"HS Kim","year":"2013","unstructured":"Kim, H. S., Joo, S. H., Cho, H. K. & Kim, Y. W. Comparison of proximal and distal cross-sectional areas of the median nerve, carpal tunnel, and nerve\/tunnel index in subjects with carpal tunnel syndrome. Arch. Phys. Med. Rehabil. 94, 2151\u20132156 (2013).","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"97043_CR46","doi-asserted-by":"publisher","first-page":"531","DOI":"10.7863\/jum.1991.10.10.531","volume":"10","author":"W Buchberger","year":"1991","unstructured":"Buchberger, W., Schon, G., Strasser, K. & Jungwirth, W. High-resolution ultrasonography of the carpal tunnel. J. Ultrasound Med. 10, 531\u2013537 (1991).","journal-title":"J. Ultrasound Med."},{"key":"97043_CR47","first-page":"1058","volume-title":"Electrodiagnostic Medicine","author":"D Dumitru","year":"2002","unstructured":"Dumitru, D., Amato, A. A. & Zwarts, M. J. Electrodiagnostic Medicine 2nd edn, 1058\u20131070 (Hanley & Belfus, 2002).","edition":"2"},{"key":"97043_CR48","unstructured":"Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. (2020)."},{"key":"97043_CR49","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-4. (2020)."},{"key":"97043_CR50","unstructured":"Greenwell, B., Boehmke, B., Cunningham, J. & GBM Developers gbm: Generalized Boosted Regression Models. R package version 2.1.8. (2020)."},{"key":"97043_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v011.i09","volume":"11","author":"A Karatzoglou","year":"2004","unstructured":"Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. kernlab - An S4 package for kernel methods in R. J. Stat. Softw. 11, 1\u201320 (2004).","journal-title":"J. Stat. Softw."},{"key":"97043_CR52","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A. & Wiener, M. Classification and Regression by randomForest. R News 2, 18\u201322 (2002).","journal-title":"R News"},{"key":"97043_CR53","doi-asserted-by":"crossref","unstructured":"Chen, T. et al. xgboost: Extreme Gradient Boosting. R package version 1.3.2.1. (2021).","DOI":"10.1155\/2021\/3771640"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97043-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97043-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97043-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T15:20:01Z","timestamp":1670080801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97043-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,31]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["97043"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-97043-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,31]]},"assertion":[{"value":"15 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17464"}}