The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
International Framework for Red Flags for Potential Serious Spinal Pathologies
Open accessAbstract
Synopsis: The International Federation of Orthopaedic Manipulative Physical Therapists (IFOMPT) led the development of a framework to help clinicians assess and manage people who may have serious spinal pathology. While rare, serious spinal pathology can have devastating and life-changing or life-limiting consequences, and must be identified early and managed appropriately. Red flags (signs and symptoms that might raise suspicion of serious spinal pathology) have historically been used by clinicians to identify serious spinal pathology. Currently, there is an absence of high-quality evidence for the diagnostic accuracy of most red flags. This framework is intended to provide a clinical-reasoning pathway to clarify the role of red flags. J Orthop Sports Phys Ther 2020;50(7):350–372. Epub 21 May 2020. doi:10.2519/jospt.2020.9971
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Spondylodiscitis: update on diagnosis and management
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Metadata
- Authors
- Finucane, L. M., Downie, A., Mercer, C., Greenhalgh, S. M., Boissonnault, W. G., Pool-Goudzwaard, A. L., Beneciuk, J. M., Leech, R. L., & Selfe, J.
- Venue
- Journal of Orthopaedic & Sports Physical Therapy
- Published
- 2020-07
- Volume
- 50
- Issue
- 7
- Pages
- 350-372
- ISSN
- 0190-6011 (ISSN-L)
- 1938-1344
- OpenAlex
- W3027361304
- Cited by
- 287
- References
- 40
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- green
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- Finucane, L. M., Downie, A., Mercer, C., Greenhalgh, S. M., Boissonnault, W. G., Pool-Goudzwaard, A. L., Beneciuk, J. M., Leech, R. L., & Selfe, J. (2020). International Framework for Red Flags for Potential Serious Spinal Pathologies. Journal of Orthopaedic & Sports Physical Therapy. https://doi.org/10.2519/jospt.2020.9971
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
