Response to Baek et al. regarding: "Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty".
- DOI
- 10.1016/j.jse.2025.07.034
- Published
- 2026 Mar
- Container
- Journal of shoulder and elbow surgery
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.jse.2025.07.034,
title = {Response to Baek et al. regarding: "Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty".},
author = {Ritter D and Denard PJ and Raiss P and Werner BC and Bedi A and Bachmaier S},
year = {2026},
journal = {Journal of shoulder and elbow surgery},
doi = {10.1016/j.jse.2025.07.034},
url = {https://doi.org/10.1016/j.jse.2025.07.034}
}RIS
TY - JOUR TI - Response to Baek et al. regarding: "Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty". AU - Ritter D AU - Denard PJ AU - Raiss P AU - Werner BC AU - Bedi A AU - Bachmaier S PY - 2026 JO - Journal of shoulder and elbow surgery DO - 10.1016/j.jse.2025.07.034 UR - https://doi.org/10.1016/j.jse.2025.07.034 ER -
APA
D, R., PJ, D., P, R., BC, W., A, B., & S, B. (2026). Response to Baek et al. regarding: "Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty".. Journal of shoulder and elbow surgery. https://doi.org/10.1016/j.jse.2025.07.034
Source records
- pubmed · retrieved 2026-09-26T11:22:18.346Z