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Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique

Crossref DOI link: https://doi.org/10.1186/s13018-024-05271-0

Published Online: 2024-11-28

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

Authors

Wu, Hao

Li, Chao

Song, Jiajun

Zhou, Jiaming
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Text and Data Mining valid from 2024-11-28

Version of Record valid from 2024-11-28
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Article History

Received: 4 April 2024

Accepted: 13 November 2024

First Online: 28 November 2024

Declarations

:

: This study was approved by the Institutional Review Board of the local medical center. Written informed consent was obtained from each participant prior each procedure.

: The authors affirm that human research participants provided informed consent for publication of the images in all Figures.

: The authors declare no competing interests.

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