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References

  1. Scavone M, Carboni E, Stefanelli E, Romano G, Vero A, Giancotti L, Miniero R, et al. Prediction of transient or permanent congenital hypothyroidism from initial thyroid stimulating hormone levels. Indian Pediatr. 2018;55: 1059–61.

    Article  Google Scholar 

  2. DeMartino L, McMahon R, Caggana M, Tavakoli NP. Gender disparities in screening for congenital hypothyroidism using thyroxine as a primary screen. Eur J Endocrinol. 2018;179:161–7.

    Article  CAS  Google Scholar 

  3. Léger J, Olivieri A, Donaldson M, Torresani T, Krude H, Van Vliet G, et al. European Society for Paediatric Endocrinology Consensus Guidelines on Screening, Diagnosis, and Management of Congenital Hypothyroidism. Hormone Res Pediatr. 2014;81:80–103.

    Article  Google Scholar 

  4. Kara C, Günindi F, Yýlmaz GC, Aydýn M. Transient congenital hypothyroidism in Turkey: An analysis on frequency and natural course. J Clin Res Pediatr Endocrinol. 2016;8:170.

    Article  Google Scholar 

  5. Bongers-Schokking JJ, Resing WC, Oostdijk W, de Rijke YB, de Muinck Keizer-Schrama SM. Relation between early over-and undertreatment and behavioural problems in preadolescent children with congenital hypothyroidism. Hormone Res Pediatr. 2018;90:247–56.

    Article  CAS  Google Scholar 

References

  1. GBD 2016 Healthcare Access and Quality Collaborators. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: A systematic analysis from the Global Burden of Disease Study 2016. Lancet. 2018; 391:2236–71.

    Article  Google Scholar 

  2. World Health Organization. WHO Global Action Plan for the Prevention And Control of Non-communicable Diseases 2013–2020. Available from: http://www.who.int/nmh/events/ncd_action_plan/en/. Accessed April 26, 2019.

  3. Maini E, Venkateswarlu B, Gupta A. Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. In: Hemant J, Fernando X, Lafate P, Baig Z(eds). International Conference on Intelligent Data Communication Technologies and Internet of Things ICICI 2018, Lecture Notes on Data Engineering and Communications Technologies. 2019(26):627–32.

    Google Scholar 

  4. Kaur H, Kumari V. Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics. 2018;doi: https://doi.org/10.1016/j.aci.2018.12.004.

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Correspondence to Rajendra Prasad Anne or Ekta Maini.

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Editor’s note: The corresponding author of the manuscript did not respond to above comments.

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Anne, R.P., Rahiman, E.A., Maini, E. et al. Correspondence. Indian Pediatr 56, 795–796 (2019). https://doi.org/10.1007/s13312-019-1629-9

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  • DOI: https://doi.org/10.1007/s13312-019-1629-9

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