Utilizing Mulligan Sustained Natural Apophyseal Glides Within a Clinical Prediction Rule for Treatment of Low Back Pain in a Secondary School Football Player

2015 ◽  
Vol 20 (1) ◽  
pp. 18-24 ◽  
Author(s):  
Jeremy Eusea ◽  
Alan Nasypany ◽  
Jeffrey Seegmiller ◽  
Russell Baker
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Secondary School ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Football Player ◽  
Low Back ◽  
Clinical Prediction

Comparison of the Effectiveness of Three Manual Physical Therapy Techniques in a Subgroup of Patients With Low Back Pain Who Satisfy a Clinical Prediction Rule

Spine ◽  
2009 ◽  
Vol 34 (25) ◽  
pp. 2720-2729 ◽  
Author(s):  
Joshua A. Cleland ◽  
Julie M. Fritz ◽  
Kornelia Kulig ◽  
Todd E. Davenport ◽  
Sarah Eberhart ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Physical Therapy ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Low Back ◽  
Clinical Prediction

Can rate of recovery be predicted in patients with acute low back pain? Development of a clinical prediction rule

2009 ◽  
Vol 13 (1) ◽  
pp. 51-55 ◽  
Author(s):  
Mark J. Hancock ◽  
Christopher G. Maher ◽  
Jane Latimer ◽  
Rob D. Herbert ◽  
James H. McAuley
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Low Back ◽  
Clinical Prediction ◽  
Acute Low Back Pain ◽  
Rate Of Recovery

A Clinical Prediction Rule To Identify Patients with Low Back Pain Most Likely To Benefit from Spinal Manipulation: A Validation Study

2004 ◽  
Vol 141 (12) ◽  
pp. 920 ◽  
Author(s):  
John D. Childs ◽  
Julie M. Fritz ◽  
Timothy W. Flynn ◽  
James J. Irrgang ◽  
Kevin K. Johnson ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Validation Study ◽  
Spinal Manipulation ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Low Back ◽  
Clinical Prediction

A Clinical Prediction Rule for Classifying Patients with Low Back Pain Who Demonstrate Short-Term Improvement With Spinal Manipulation

Spine ◽  
2002 ◽  
Vol 27 (24) ◽  
pp. 2835-2843 ◽  
Author(s):  
Timothy Flynn ◽  
Julie Fritz ◽  
Julie Whitman ◽  
Robert Wainner ◽  
Jake Magel ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Spinal Manipulation ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Low Back ◽  
Clinical Prediction ◽  
Short Term

scholarly journals Which Prognostic Factors for Low Back Pain Are Generic Predictors of Outcome Across a Range of Recovery Domains?

2013 ◽  
Vol 93 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Chad E. Cook ◽  
Kenneth E. Learman ◽  
Bryan J. O'Halloran ◽  
Christopher R. Showalter ◽  
Vincent J. Kabbaz ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Outcome Measures ◽  
Predictive Models ◽  
Rating Scale ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Low Back ◽  
Clinical Prediction ◽  
Prognostic Variables

Background Recovery from low back pain (LBP) is multidimensional and requires the use of multiple-response (outcome) measures to fully reflect these many dimensions. Predictive prognostic variables that are present or stable in all or most predictive models that use different outcome measures could be considered “universal” prognostic variables. Objective The aim of this study was to explore the potential of universal prognostic variables in predictive models for 4 different outcome measures in patients with mechanical LBP. Design Predictive modeling was performed using data extracted from a randomized controlled trial. Four prognostic models were created using backward stepwise deletion logistic, Poisson, and linear regression. Methods Data were collected from 16 outpatient physical therapy facilities in 10 states. All 149 patients with LBP were treated with manual therapy and spine strengthening exercises until discharge. Four different measures of response were used: Oswestry Disability Index and Numeric Pain Rating Scale change scores, total visits, and report of rate of recovery. Results The set of statistically significant predictors was dependent on the definition of response. All regression models were significant. Within both forms of the 4 models, meeting the clinical prediction rule for manipulation at baseline was present in all 4 models, whereas no irritability at baseline and diagnosis of sprains and strains were present in 2 of 4 of the predictive models. Limitations The primary limitation is that this study evaluated only 4 of the multiple outcome measures that are pertinent for patients with LBP. Conclusions Meeting the clinical prediction rule was prognostic for all outcome measures and should be considered a universal prognostic predictor. Other predictive variables were dependent on the outcomes measure used in the predictive model.

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