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dc.contributor.authorSmart, Keith M
dc.contributor.authorBlake, Catherine
dc.contributor.authorStaines, Anthony
dc.contributor.authorThacker, Mick
dc.contributor.authorDoody, Catherine
dc.date.accessioned2012-11-07T15:08:36Z
dc.date.available2012-11-07T15:08:36Z
dc.date.issued2012-08
dc.identifier.citationMechanisms-based classifications of musculoskeletal pain: part 2 of 3: symptoms and signs of peripheral neuropathic pain in patients with low back (± leg) pain. 2012, 17 (4):345-51 Man Theren_GB
dc.identifier.issn1532-2769
dc.identifier.pmid22465002
dc.identifier.doi10.1016/j.math.2012.03.003
dc.identifier.urihttp://hdl.handle.net/10147/251281
dc.description.abstractAs a mechanisms-based classification of pain 'peripheral neuropathic pain' (PNP) refers to pain arising from a primary lesion or dysfunction in the peripheral nervous system. Symptoms and signs associated with an assumed dominance of PNP in patients attending for physiotherapy have not been extensively studied. The purpose of this study was to identify symptoms and signs associated with a clinical classification of PNP in patients with low back (± leg) pain. Using a cross-sectional, between-subjects design; four hundred and sixty-four patients with low back (± leg) pain were assessed using a standardised assessment protocol. Patients' pain was assigned a mechanisms-based classification based on experienced clinical judgement. Clinicians then completed a clinical criteria checklist specifying the presence or absence of various clinical criteria. A binary logistic regression analysis with Bayesian model averaging identified a cluster of two symptoms and one sign predictive of PNP, including: 'Pain referred in a dermatomal or cutaneous distribution', 'History of nerve injury, pathology or mechanical compromise' and 'Pain/symptom provocation with mechanical/movement tests (e.g. Active/Passive, Neurodynamic) that move/load/compress neural tissue'. This cluster was found to have high levels of classification accuracy (sensitivity 86.3%, 95% CI: 78.0-92.3; specificity 96.0%, 95% CI: 93.4-97.8; diagnostic odds ratio 150.9, 95% CI: 69.4-328.1). Pattern recognition of this empirically-derived cluster of symptoms and signs may help clinicians identify an assumed dominance of PNP mechanisms in patients with low back pain disorders in a way that might usefully inform subsequent patient management.
dc.language.isoenen
dc.rightsArchived with thanks to Manual therapyen_GB
dc.subject.meshAdult
dc.subject.meshAged
dc.subject.meshAged, 80 and over
dc.subject.meshAnalysis of Variance
dc.subject.meshBayes Theorem
dc.subject.meshCluster Analysis
dc.subject.meshCross-Sectional Studies
dc.subject.meshFemale
dc.subject.meshHumans
dc.subject.meshIreland
dc.subject.meshLow Back Pain
dc.subject.meshMale
dc.subject.meshMiddle Aged
dc.subject.meshMusculoskeletal Pain
dc.subject.meshNociceptive Pain
dc.subject.meshPeripheral Nervous System Diseases
dc.subject.meshSensitivity and Specificity
dc.subject.meshSeverity of Illness Index
dc.subject.meshYoung Adult
dc.titleMechanisms-based classifications of musculoskeletal pain: part 2 of 3: symptoms and signs of peripheral neuropathic pain in patients with low back (± leg) pain.en_GB
dc.typeArticleen
dc.contributor.departmentPhysiotherapy Department, St Vincent's University Hospital, Elm Park, Dublin 4, Ireland. k.smart@svuh.ieen_GB
dc.identifier.journalManual therapyen_GB
dc.description.provinceLeinsteren
refterms.dateFOA2018-08-23T01:42:17Z
html.description.abstractAs a mechanisms-based classification of pain 'peripheral neuropathic pain' (PNP) refers to pain arising from a primary lesion or dysfunction in the peripheral nervous system. Symptoms and signs associated with an assumed dominance of PNP in patients attending for physiotherapy have not been extensively studied. The purpose of this study was to identify symptoms and signs associated with a clinical classification of PNP in patients with low back (± leg) pain. Using a cross-sectional, between-subjects design; four hundred and sixty-four patients with low back (± leg) pain were assessed using a standardised assessment protocol. Patients' pain was assigned a mechanisms-based classification based on experienced clinical judgement. Clinicians then completed a clinical criteria checklist specifying the presence or absence of various clinical criteria. A binary logistic regression analysis with Bayesian model averaging identified a cluster of two symptoms and one sign predictive of PNP, including: 'Pain referred in a dermatomal or cutaneous distribution', 'History of nerve injury, pathology or mechanical compromise' and 'Pain/symptom provocation with mechanical/movement tests (e.g. Active/Passive, Neurodynamic) that move/load/compress neural tissue'. This cluster was found to have high levels of classification accuracy (sensitivity 86.3%, 95% CI: 78.0-92.3; specificity 96.0%, 95% CI: 93.4-97.8; diagnostic odds ratio 150.9, 95% CI: 69.4-328.1). Pattern recognition of this empirically-derived cluster of symptoms and signs may help clinicians identify an assumed dominance of PNP mechanisms in patients with low back pain disorders in a way that might usefully inform subsequent patient management.


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