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Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain

Crossref DOI link: https://doi.org/10.1186/s12891-024-07485-z

Published Online: 2024-05-13

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Hwang, Ui-jae

Kwon, Oh-yun

Kim, Jun-hee
Funding

Funding for this research was provided by:

Yonsei University Research Fund (2023-51-0199)
License Information

Text and Data Mining valid from 2024-05-13

Version of Record valid from 2024-05-13
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Article History

Received: 10 January 2024

Accepted: 2 May 2024

First Online: 13 May 2024

Declarations

:

: The present study conformed to the ethical guidelines of the 1975 Declarations of Helsinki. The requirement for informed consent was waived by Yonsei University Mirae Campus Institutional review board approval (1041849-202306-BM-100-01) because of the retrospective nature of the study and the study’s reliance on preexisting data acquired from investigating the risk factors of musculoskeletal disorders. Ethical approval was obtained from Yonsei University Mirae Campus Institutional review board.

: Written informed consent was obtained from patient to authorize the publication. Informed consent was obtained from subject for publication of identifying image in an online open-access publication (when applicable).

: The authors declare that they have no potential conflicts of interest with respect to the research, authorship, and publication of this article. The results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

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