CORRECTION article

Front. Med., 09 August 2022

Sec. Nephrology

Volume 9 - 2022 | https://doi.org/10.3389/fmed.2022.964157

Corrigendum: Prediction model of immunosuppressive medication non-adherence for renal transplant patients based on machine learning technology

  • 1. Nursing School of Central South University, Changsha, China

  • 2. Nursing Department of Third Xiangya Hospital of Central South University, Changsha, China

  • 3. Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital, Central South University, Changsha, China

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In our published article, there was an error in Table 2 as published. Table 2 used a scale Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS), which was authorized by the original developer Dr. De Geest. Dr. De Geest contacted us recently. He suggested that the Table 2 should be presented like their team. Therefore, we would like to replace Table 2. The corrected Table 2 and its caption appear below.

Table 2

Item numberNo. (%)
1ATaking non-adherence: Yes/No218 (21.6) / 793 (78.4)
1 occasion166 (16.4)
2 or more occasions52 (5.2)
1BDrug-holidays: Yes / No122 (12.1) / 889 (87.9)
1 occasion94 (9.3)
2 or more occasions28 (2.8)
2Timing adherence: Yes/No281 (27.8) / 730 (72.2)
1 occasion151 (14.9)
2–3 occasions98 (9.7)
4–5 occasions15 (1.5)
Every 2–3 days14 (1.4)
Almost every day3 (0.3)
3Dose-alteration: Yes/No62 (6.2) / 949 (93.8)
4Discontinuation Yes/No33 (3.3) / 978 (96.7)

Adherence to IM measured by BAASIS.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Summary

Keywords

immunosuppressive medication, non-adherence, prediction model, renal transplant patients, machine learning technology

Citation

Zhu X, Peng B, Yi Q, Liu J and Yan J (2022) Corrigendum: Prediction model of immunosuppressive medication non-adherence for renal transplant patients based on machine learning technology. Front. Med. 9:964157. doi: 10.3389/fmed.2022.964157

Received

08 June 2022

Accepted

18 July 2022

Published

09 August 2022

Volume

9 - 2022

Edited and reviewed by

Hoon Young Choi, Yonsei University, South Korea

Updates

Copyright

*Correspondence: QiFeng Yi Jia Liu

†These authors have contributed equally to this work and share first authorship

This article was submitted to Nephrology, a section of the journal Frontiers in Medicine

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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