Literature DB >> 15800512

Reliability of McKenzie classification of patients with cervical or lumbar pain.

Helen A Clare1, Roger Adams, Christopher G Maher.   

Abstract

BACKGROUND: In the McKenzie system, patients are classified first into syndromes, then into subsyndromes. At present, the reliability of classification with this system is unclear. No study has included patients with cervical pain, and the studies to date have reported conflicting results.
OBJECTIVE: The aim of the study is to investigate the interexaminer reliability of the McKenzie classification system for patients with cervical or lumbar pain.
SUBJECTS: Fifty patients with spinal pain (25 with lumbar pain and 25 with cervical pain) were included in the study.
METHOD: The patients were assessed simultaneously by 2 physical therapists (14 in total) trained in the McKenzie method. Agreement was expressed using the multirater kappa coefficient and percent agreement for classification into (i) syndromes and (ii) subsyndromes.
RESULTS: The reliability for syndrome classification was kappa = 0.84 with 96% agreement for the total patient pool, kappa = 1.0 with 100% agreement for lumbar patients, and kappa = 0.63 with 92% agreement for cervical patients. The reliability for subsyndrome classification was kappa = 0.87 with 90% agreement for the total patient pool, kappa = 0.89 with 92% agreement for lumbar patients, and kappa = 0.84 with 88% agreement for the cervical patients.
CONCLUSION: The McKenzie assessment performed by persons trained in the McKenzie method may allow for reliable classification of patients with lumbar and cervical pain.

Entities:  

Mesh:

Year:  2005        PMID: 15800512     DOI: 10.1016/j.jmpt.2005.01.003

Source DB:  PubMed          Journal:  J Manipulative Physiol Ther        ISSN: 0161-4754            Impact factor:   1.437


  28 in total

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8.  Inter-rater reliability of a modified version of Delitto et al.'s classification-based system for low back pain: a pilot study.

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9.  Exploring the contribution of patient-reported and clinician based variables for the prediction of low back work status.

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