Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of t...
Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study
About this item
Full title
Author / Creator
Belavy, Daniel L. , Tagliaferri, Scott D. , Tegenthoff, Martin , Enax-Krumova, Elena , Schlaffke, Lara , Bühring, Björn , Schulte, Tobias L. , Schmidt, Sein , Wilke, Hans-Joachim , Angelova, Maia , Trudel, Guy , Ehrenbrusthoff, Katja , Fitzgibbon, Bernadette , Van Oosterwijck, Jessica , Miller, Clint T. , Owen, Patrick J. , Bowe, Steven , Döding, Rebekka and Kaczorowski, Svenja
Publisher
San Francisco: Public Library of Science
Journal title
Language
English
Formats
Publication information
Publisher
San Francisco: Public Library of Science
Subjects
More information
Scope and Contents
Contents
In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that i...
Alternative Titles
Full title
Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study
Authors, Artists and Contributors
Author / Creator
Tagliaferri, Scott D.
Tegenthoff, Martin
Enax-Krumova, Elena
Schlaffke, Lara
Bühring, Björn
Schulte, Tobias L.
Schmidt, Sein
Wilke, Hans-Joachim
Angelova, Maia
Trudel, Guy
Ehrenbrusthoff, Katja
Fitzgibbon, Bernadette
Van Oosterwijck, Jessica
Miller, Clint T.
Owen, Patrick J.
Bowe, Steven
Döding, Rebekka
Kaczorowski, Svenja
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_plos_journals_2854332792
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2854332792
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0282346
