What Characterizes People Who Have an Unclear Classification Using a Treatment-Based Classification Algorithm for Low Back Pain? A Cross-Sectional Study | Zendy

Tasha R. Stanton | Zendy; Mark J. Hancock | Zendy; Adri T. Apeldoorn | Zendy; Benedict M. Wand | Zendy; Julie M. Fritz | Zendy

Open Access

What Characterizes People Who Have an Unclear Classification Using a Treatment-Based Classification Algorithm for Low Back Pain? A Cross-Sectional Study

Author(s) -

Tasha R. Stanton,

Mark J. Hancock,

Adri T. Apeldoorn,

Benedict M. Wand,

Julie M. Fritz

Publication year - 2012

Publication title -

physical therapy

Language(s) - English

Resource type - Journals

eISSN - 1538-6724

pISSN - 0031-9023

DOI - 10.2522/ptj.20120263

Subject(s) - cross sectional study , algorithm , computer science , medicine , artificial intelligence , physical medicine and rehabilitation , machine learning , physical therapy , psychology , pathology

A treatment-based classification algorithm for low back pain (LBP) was created to help clinicians select treatments to which people are most likely to respond. To allow the algorithm to classify all people with LBP, additional criteria can help therapists make decisions for people who do not clearly fit into a subgroup (ie, unclear classifications). Recent studies indicated that classifications are unclear for approximately 34% of people with LBP.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research