{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T15:06:55Z","timestamp":1789225615639,"version":"build-2803163510"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"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":["Eur J Pediatr"],"DOI":"10.1007\/s00431-026-07315-5","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T02:49:59Z","timestamp":1786675799000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study"],"prefix":"10.1007","volume":"185","author":[{"given":"Azita","family":"Yazdani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Hadi Hoseyni Jahan","family":"Abadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parisa","family":"Eslami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Tadayon Chahar","family":"Soughi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"gholami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5094-6385","authenticated-orcid":false,"given":"Leila","family":"Erfannia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"7315_CR1","doi-asserted-by":"crossref","unstructured":"Nabila M, Baidani A, Mourajid Y, Chebabe M, Abderraouf HJPr (2024) Analysis of risk determinants of neonatal mortality in the last decade: a systematic literature review (2013\u20132023). 16(3):696\u2013716","DOI":"10.3390\/pediatric16030059"},{"key":"7315_CR2","doi-asserted-by":"crossref","unstructured":"Stoll BJ, Hansen NI, Bell EF, Walsh MC, Carlo WA, Shankaran S, et al (2015) Trends in care practices, morbidity, and mortality of extremely preterm neonates, 1993\u20132012. 314(10):1039\u201351","DOI":"10.1001\/jama.2015.10244"},{"issue":"2","key":"7315_CR3","first-page":"491","volume":"62","author":"RP Santos","year":"2015","unstructured":"Santos RP (2015) Tristram DJPC. A practical guide to the diagnosis, treatment, and prevention of neonatal infections 62(2):491\u2013508","journal-title":"A practical guide to the diagnosis, treatment, and prevention of neonatal infections"},{"key":"7315_CR4","doi-asserted-by":"publisher","unstructured":"Attia Hussein Mahmoud H, Parekh R, Dhandibhotla S, Sai T, Pradhan A, Alugula S, et al. Insight into neonatal sepsis: an overview. Cureus. 2023;15(9):e45530. Epub 2023\/10\/23. https:\/\/doi.org\/10.7759\/cureus.45530","DOI":"10.7759\/cureus.45530"},{"issue":"8","key":"7315_CR5","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1136\/archdischild-2020-320217","volume":"106","author":"C Fleischmann","year":"2021","unstructured":"Fleischmann C, Reichert F, Cassini A, Horner R, Harder T, Markwart R et al (2021) Global incidence and mortality of neonatal sepsis: a systematic review and meta-analysis. Arch Dis Child 106(8):745\u2013752","journal-title":"Arch Dis Child"},{"issue":"1","key":"7315_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0227570","volume":"15","author":"Z Akbarian-Rad","year":"2020","unstructured":"Akbarian-Rad Z, Riahi SM, Abdollahi A, Sabbagh P, Ebrahimpour S, Javanian M et al (2020) Neonatal sepsis in Iran: a systematic review and meta-analysis on national prevalence and causative pathogens. PLoS One 15(1):e0227570","journal-title":"PLoS One"},{"key":"7315_CR7","doi-asserted-by":"publisher","unstructured":"Habibelahi A, Heidarzadeh M, Abdollahi L, Taheri M, Ghaffari-Fam S, Vakilian R, et al. Clinical cause of neonatal mortality in Iran: analysis of the national Iranian Maternal And Neonatal network. BMJ Paediatr Open. 2024;8(1). Epub 20240320. https:\/\/doi.org\/10.1136\/bmjpo-2023-002315","DOI":"10.1136\/bmjpo-2023-002315"},{"issue":"3","key":"7315_CR8","doi-asserted-by":"publisher","first-page":"124","DOI":"10.4103\/2249-4847.101692","volume":"1","author":"S Afsharpaiman","year":"2012","unstructured":"Afsharpaiman S, Torkaman M, Saburi A, Farzaampur A, Amirsalari S, Kavehmanesh Z (2012) Trends in incidence of neonatal sepsis and antibiotic susceptibility of causative agents in two neonatal intensive care units in Tehran, IR Iran. J Clin Neonatol 1(3):124\u2013130","journal-title":"J Clin Neonatol"},{"issue":"2","key":"7315_CR9","first-page":"251","volume":"48","author":"E Sewell","year":"2021","unstructured":"Sewell E, Roberts J (2021) Mukhopadhyay SJCip. Association of infection in the neonate and long-term neurodevelopmental outcome 48(2):251","journal-title":"Association of infection in the neonate and long-term neurodevelopmental outcome"},{"issue":"10449","key":"7315_CR10","first-page":"277","volume":"404","author":"T Strunk","year":"2024","unstructured":"Strunk T, Molloy EJ, Mishra A, Bhutta ZAJTL (2024) Neonatal bacterial sepsis 404(10449):277\u2013293","journal-title":"Neonatal bacterial sepsis"},{"key":"7315_CR11","doi-asserted-by":"crossref","unstructured":"Feng S, Lv X, Dai XJAoPM (2023) Systematic review and meta-analysis of the efficacy and safety of cerebrospinal fluid drainage and lumbar puncture in the treatment of cerebrospinal fluid leakage after craniocerebral injury. 12(1):12132","DOI":"10.21037\/apm-22-1302"},{"key":"7315_CR12","doi-asserted-by":"crossref","unstructured":"Kim SSJC, Pediatrics E (2022) Lumbar puncture or not: when does febrile seizure require a neurodiagnostic evaluation? 2022;66(2):68","DOI":"10.3345\/cep.2022.01081"},{"issue":"7","key":"7315_CR13","first-page":"422","volume":"103","author":"B Shahan","year":"2021","unstructured":"Shahan B, Choi EY (2021) Nieves GJAfp. Cerebrospinal fluid analysis 103(7):422\u2013428","journal-title":"Cerebrospinal fluid analysis"},{"issue":"1","key":"7315_CR14","first-page":"49","volume":"21","author":"MA Glaser","year":"2021","unstructured":"Glaser MA, Hughes LM, Jnah A (2021) Newberry DJAinc. Neonatal sepsis: a review of pathophysiology and current management strategies 21(1):49\u201360","journal-title":"Neonatal sepsis: a review of pathophysiology and current management strategies"},{"key":"7315_CR15","doi-asserted-by":"crossref","unstructured":"Hrishi AP, Sethuraman MJIjoccmp-r (2019) official publication of Indian Society of Critical Care Medicine. Cerebrospinal fluid (CSF) analysis and interpretation in neurocritical care for acute neurological conditions. 2019;23(Suppl 2):S115","DOI":"10.5005\/jp-journals-10071-23187"},{"issue":"4","key":"7315_CR16","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.jhin.2008.01.011","volume":"68","author":"A Borghesi","year":"2008","unstructured":"Borghesi A, Stronati M (2008) Strategies for the prevention of hospital-acquired infections in the neonatal intensive care unit. J Hosp Infect 68(4):293\u2013300","journal-title":"J Hosp Infect"},{"issue":"1","key":"7315_CR17","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s12879-025-10525-1","volume":"25","author":"BT Oyato","year":"2025","unstructured":"Oyato BT, Sime TK, Debele T, Abasimel HZ, Alemu FT, Asfaw SB et al (2025) Time to recovery of neonatal sepsis and its predictors in public hospitals of North Shoa Zone, Central Ethiopia. BMC Infect Dis 25(1):113","journal-title":"BMC Infect Dis"},{"key":"7315_CR18","unstructured":"Ajanovic S, Jobst B, Jim\u00e9nez J, Quesada R, Santos F, Carandell F, et al (2025) Non-invasive meningitis screening in neonates and infants: multicentre international study. 2025:1\u201311"},{"issue":"9","key":"7315_CR19","first-page":"199","volume":"9","author":"DU De Rose","year":"2024","unstructured":"De Rose DU, Ronchetti MP, Martini L, Rechichi J, Iannetta M, Dotta A et al (2024) Diagnosis and management of neonatal bacterial sepsis: current challenges and future perspectives 9(9):199","journal-title":"Diagnosis and management of neonatal bacterial sepsis: current challenges and future perspectives"},{"key":"7315_CR20","volume":"9","author":"FM Keij","year":"2021","unstructured":"Keij FM, Achten NB, Tramper-Stranders GA, Allegaert K, Van Rossum AM, Reiss IK et al (2021) Stratified management for bacterial infections in late preterm and term neonates: current strategies and future opportunities toward precision medicine 9:590969","journal-title":"Stratified management for bacterial infections in late preterm and term neonates: current strategies and future opportunities toward precision medicine"},{"issue":"1","key":"7315_CR21","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1186\/s12911-023-02335-9","volume":"23","author":"A Yazdani","year":"2023","unstructured":"Yazdani A, Shanbehzadeh M, Kazemi-Arpanahi H (2023) Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms. BMC Med Inform Decis Mak 23(1):229","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"7315_CR22","doi-asserted-by":"publisher","DOI":"10.1177\/14604582231152792","volume":"29","author":"A Yazdani","year":"2023","unstructured":"Yazdani A, Dashti SF, Safdari Y (2023) A fog-assisted information model based on priority queue and clinical decision support systems. Health Informatics J 29(1):14604582231152792","journal-title":"Health Informatics J"},{"key":"7315_CR23","doi-asserted-by":"crossref","unstructured":"Nopour R, Erfannia L, Mehrabi N, Mashoufi M, Mahdavi A, Shanbehzadeh MJSE-MJ (2022) Comparison of two statistical models for predicting mortality in COVID-19 patients in Iran. 2022;23(6)","DOI":"10.5812\/semj.119172"},{"issue":"4","key":"7315_CR24","doi-asserted-by":"publisher","DOI":"10.1177\/14604582221137453","volume":"28","author":"A Golkar","year":"2022","unstructured":"Golkar A, Malekhosseini R, RahimiZadeh K, Yazdani A, Beheshti A (2022) A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments. Health Informatics J 28(4):14604582221137453","journal-title":"Health Informatics J"},{"issue":"1","key":"7315_CR25","first-page":"7567","volume":"11","author":"DJ Park","year":"2021","unstructured":"Park DJ, Park MW, Lee H, Kim Y-J, Kim Y (2021) Park YHJSr. Development of machine learning model for diagnostic disease prediction based on laboratory tests 11(1):7567","journal-title":"Development of machine learning model for diagnostic disease prediction based on laboratory tests"},{"issue":"2","key":"7315_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/jpm10020021","volume":"10","author":"G Battineni","year":"2020","unstructured":"Battineni G, Sagaro GG, Chinatalapudi N, Amenta FJ (2020) Applications of machine learning predictive models in the chronic disease diagnosis. Journal of Personalized Medicine 10(2):21","journal-title":"Journal of Personalized Medicine"},{"issue":"1","key":"7315_CR27","doi-asserted-by":"publisher","DOI":"10.1136\/bmjpo-2025-003561","volume":"10","author":"D Kainth","year":"2026","unstructured":"Kainth D, Gupta A, Singh P, Prakash S, Thukral A, Deorari A et al (2026) A machine learning model for prediction of early-onset neonatal sepsis in low-income and middle-income countries: development and validation study. BMJ Paediatrics Open 10(1):e003561","journal-title":"BMJ Paediatrics Open"},{"key":"7315_CR28","doi-asserted-by":"publisher","DOI":"10.2174\/0118750362416494251124105928","author":"Z Chi","year":"2026","unstructured":"Chi Z, Qin Z (2026) Early detection of neonatal infection in NICU using machine learning models: a retrospective observational cohort study. Open Bioinformatics J. https:\/\/doi.org\/10.2174\/0118750362416494251124105928","journal-title":"Open Bioinformatics J"},{"issue":"1","key":"7315_CR29","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/3567194","volume":"2023","author":"YG Robi","year":"2023","unstructured":"Robi YG, Sitote TM (2023) Neonatal disease prediction using machine learning techniques. J Healthc Eng 2023(1):3567194","journal-title":"J Healthc Eng"},{"issue":"6","key":"7315_CR30","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1111\/ijlh.13549","volume":"43","author":"B Huang","year":"2021","unstructured":"Huang B, Wang R, Masino AJ, Obstfeld AE (2021) Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques. Int J Lab Hematol 43(6):1341\u20131356","journal-title":"Int J Lab Hematol"},{"key":"7315_CR31","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2023.1158352","volume":"14","author":"E Parkinson","year":"2023","unstructured":"Parkinson E, Liberatore F, Watkins WJ, Andrews R, Edkins S, Hibbert J et al (2023) Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data. Front Genet 14:1158352","journal-title":"Front Genet"},{"issue":"1","key":"7315_CR32","volume":"3","author":"L Cabrera-Quiros","year":"2021","unstructured":"Cabrera-Quiros L, Kommers D, Wolvers MK, Oosterwijk L, Arents N, van der Sluijs-Bens J et al (2021) Prediction of late-onset sepsis in preterm infants using monitoring signals and machine learning. Crit Care Explor 3(1):e0302","journal-title":"Crit Care Explor"},{"issue":"2","key":"7315_CR33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0212665","volume":"14","author":"AJ Masino","year":"2019","unstructured":"Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP et al (2019) Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS ONE 14(2):e0212665","journal-title":"PLoS ONE"},{"issue":"9","key":"7315_CR34","first-page":"1875","volume":"10","author":"I-M Chiu","year":"2021","unstructured":"Chiu I-M, Cheng C-Y, Zeng W-H, Huang Y-H (2021) Lin C-HRJJoCM. Using machine learning to predict invasive bacterial infections in young febrile infants visiting the emergency department 10(9):1875","journal-title":"Using machine learning to predict invasive bacterial infections in young febrile infants visiting the emergency department"},{"key":"7315_CR35","doi-asserted-by":"publisher","unstructured":"Ramgopal S, Horvat CM, Yanamala N, Alpern ER. Machine learning to predict serious bacterial infections in young febrile infants. Pediatrics. 2020;146(3). Epub 2020\/08\/29. https:\/\/doi.org\/10.1542\/peds.2019-4096","DOI":"10.1542\/peds.2019-4096"},{"issue":"9","key":"7315_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10091875","volume":"10","author":"I-M Chiu","year":"2021","unstructured":"Chiu I-M, Cheng C-Y, Zeng W-H, Huang Y-H, Lin C-H (2021) Using machine learning to predict invasive bacterial infections in young febrile infants visiting the emergency department. J Clin Med 10(9):1875","journal-title":"J Clin Med"},{"issue":"7","key":"7315_CR37","doi-asserted-by":"publisher","DOI":"10.2196\/15965","volume":"8","author":"W Song","year":"2020","unstructured":"Song W, Jung SY, Baek H, Choi CW, Jung YH, Yoo S (2020) A predictive model based on machine learning for the early detection of late-onset neonatal sepsis: development and observational study. JMIR Med Inform 8(7):e15965","journal-title":"JMIR Med Inform"},{"issue":"3","key":"7315_CR38","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2019-4096","volume":"146","author":"S Ramgopal","year":"2020","unstructured":"Ramgopal S, Horvat CM, Yanamala N, Alpern ER (2020) Machine learning to predict serious bacterial infections in young febrile infants. Pediatrics 146(3):e20194096","journal-title":"Pediatrics"},{"key":"7315_CR39","doi-asserted-by":"crossref","unstructured":"G\u00f3mez R, Garc\u00eda N, Collantes G, Ponce F, Redon P, editors. Development of a non-invasive procedure to early detect neonatal sepsis using HRV monitoring and machine learning algorithms. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS); 2019: IEEE","DOI":"10.1109\/CBMS.2019.00037"},{"issue":"2","key":"7315_CR40","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1136\/amiajnl-2013-001854","volume":"21","author":"S Mani","year":"2014","unstructured":"Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R et al (2014) Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc 21(2):326\u2013336","journal-title":"J Am Med Inform Assoc"},{"key":"7315_CR41","unstructured":"Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, et al., editors. The CRISP-DM user guide. 4th CRISP-DM SIG Workshop in Brussels in March; 1999: sn"},{"key":"7315_CR42","first-page":"526","volume":"181","author":"C Schr\u00f6er","year":"2021","unstructured":"Schr\u00f6er C, Kruse F (2021) G\u00f3mez JMJPcs. A systematic literature review on applying CRISP-DM process model 181:526\u2013534","journal-title":"A systematic literature review on applying CRISP-DM process model"},{"key":"7315_CR43","unstructured":"(ISSO) ISSO. Iranian Social Security Organization (ISSO) History: en.tamin.ir; 2025 [December 13,2025]. Available from: https:\/\/en.tamin.ir\/news\/98428.html"},{"key":"7315_CR44","first-page":"1130229","volume":"13","author":"HH Rashidi","year":"2023","unstructured":"Rashidi HH, Albahra S, Robertson S, Tran NK (2023) Hu BJFiO. Common statistical concepts in the supervised Machine Learning arena 13:1130229","journal-title":"Common statistical concepts in the supervised Machine Learning arena"},{"key":"7315_CR45","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied logistic regression","author":"DW Hosmer","year":"2013","unstructured":"Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons"},{"key":"7315_CR46","doi-asserted-by":"publisher","unstructured":"Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 10(3):e0118432. Epub 20150304. https:\/\/doi.org\/10.1371\/journal.pone.0118432","DOI":"10.1371\/journal.pone.0118432"},{"key":"7315_CR47","doi-asserted-by":"publisher","unstructured":"Rufibach K. Use of Brier score to assess binary predictions. J Clin Epidemiol. 2010;63(8):938\u20139; author reply 9. Epub 20100301. https:\/\/doi.org\/10.1016\/j.jclinepi.2009.11.009. PubMed PMID: 20189763.","DOI":"10.1016\/j.jclinepi.2009.11.009"},{"issue":"1","key":"7315_CR48","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","volume":"21","author":"EW Steyerberg","year":"2010","unstructured":"Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N et al (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21(1):128\u2013138","journal-title":"Epidemiology"},{"issue":"10","key":"7315_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/covid5100167","volume":"5","author":"A Prado","year":"2025","unstructured":"Prado A, Cobre A, Volanski W, Signorini L, Valdameri G, Moure VR et al (2025) Machine learning models to discriminate COVID-19 severity with biomarkers available in Brazilian public health. COVID 5(10):167","journal-title":"COVID"}],"container-title":["European Journal of Pediatrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00431-026-07315-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00431-026-07315-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00431-026-07315-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T15:02:39Z","timestamp":1789225359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00431-026-07315-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,14]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["7315"],"URL":"https:\/\/doi.org\/10.1007\/s00431-026-07315-5","relation":{},"ISSN":["1432-1076"],"issn-type":[{"value":"1432-1076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,14]]},"assertion":[{"value":"7 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The study protocol was approved before the initiation of this study by the Ethics Committee of Shiraz University of Medical Sciences (approval ID: IR.SUMS.NUMIMG.REC.1402.007). The study was performed in compliance with this institutional guideline, ethical guidelines for clinical research of the Iran government. Participants gave their informed consent to participate.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"664"}}