Reliability of Detecting a Relevant Lateral Shift in Patients with Lumbar Derangement: A Pilot Study

Seymour, Ron; Walsh, Thom; Blankenberg, Chris; Pickens, Anthony; Rush, Heath

doi:10.1179/106698102790819193

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Eight studies looked at the inter-tester reliability and classification ability of the McKenzie method (Supplementary Table 7 ) 113 , 115 , 121 , 122 , 131 – 133 , 136 . Overall, seven studies assessed the reliability with a Kappa value range of 0.02−1.00 113 , 121 , 122 , 131 – 133 , 136 . Only two of these studies had Kappa ranges >0.6; thus, five studies had poor to moderate agreement 140 .…”

Section: Resultsmentioning

confidence: 99%

“…The reliability (i.e. the consistency of the classification system over repeated attempts with the same patient) 146 of the McKenzie method was poor to moderate 113,115,121,122,[131][132][133]136 and moderate to excellent for the STarT Back tool 74,75,82,87,98,99,101,103,109 . This limits the ability of the McKenzie method to be a useful classification system for people with LBP, as this impacts the ability to identify a movement or structure that benefits from a specific treatment 141 .…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in lowback pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific subgroups , and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test−retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter-and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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“…Furthermore, there is evidence to suggest that practitioners are not able to reliably determine, through observation alone, whether or not a patient is shifted in the frontal plane. [13][14][15][16] Several authors 13,17,18 have looked at the interrater reliability of physical therapists to determine whether or not a lateral shift, or a RLC, existed in a group of patients with LBP. The percent agreement in these studies ranged from fair to good.…”

mentioning

confidence: 99%

A mechanical diagnosis and treatment (MDT) approach for a patient with discogenic low back pain and a relevant lateral component: a case report

Williams

Vaughn

Holwerda

2011

Journal of Manual & Manipulative Therapy

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Patients with a lateral lumbar shift are often managed with a frontal plane, manual correction first described by McKenzie. However, there is a subset of patients that require a frontal plane correction who do not demonstrate a lateral shift. Both of these patient groups have what is termed a relevant lateral component (RLC). The subset of patients who have an RLC, without a shift, has received very little attention in the literature to date. This case report describes a patient with no shift who had a remarkable disc extrusion identified by magnetic resonance imaging (MRI). The patient responded nicely to a combination of frontal and transverse plane loading strategies in six treatments using the mechanical diagnosis and treatment (MDT) evaluation and treatment system. The case report is unique in the literature in that it describes a patient with no apparent frontal plane, lumbar deformity that had documented evidence of a large disc extrusion and who responded to a variation of McKenzie's flexion-rotation mobilization. The centralization phenomenon was demonstrated immediately when a non-weight bearing, multiplanar, loading strategy was administered.

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Reliability of procedures used in the physical examination of non-specific low back pain: A systematic review

May

Littlewood

Bishop

2006

Australian Journal of Physiotherapy

138

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The purpose of this systematic review was to determine the quality of the research and to assess the reliability of different types of physical examination procedures used in the assessment of patients with non-specific low back pain. A search of electronic databases (MEDLINE, PEDro, AMED, EMBASE, Cochrane, and CINAHL) up to August 2005 identified 48 relevant studies which were analysed for quality and reliability. Pre-established criteria were used to judge the quality of the studies and satisfactory reliability, and conclusions emphasised high quality studies (> or = 60% methods score). The mean quality score of the studies was 52% (range 0 to 88%), indicating weak to moderate methodology. Based on the upper threshold used (kappa/ICC > 0.85) most procedures demonstrated either conflicting evidence or moderate to strong evidence of low reliability. When the lower threshold was used (kappa/ICC > 0.70) evidence about pain response to repeated movements changed from contradictory to moderate evidence for high reliability. Most procedures commonly used by clinicians in the examination of patients with back pain demonstrate low reliability.

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Reliability of Detecting a Relevant Lateral Shift in Patients with Lumbar Derangement: A Pilot Study

Cited by 4 publications

References 15 publications

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

A mechanical diagnosis and treatment (MDT) approach for a patient with discogenic low back pain and a relevant lateral component: a case report

Reliability of procedures used in the physical examination of non-specific low back pain: A systematic review

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