Features and methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system: a Delphi expert consensus study
Classification of musculoskeletal pain based on underlying pain mechanisms (nociceptive, neuropathic, and nociplastic pain) is challenging. In the absence of a gold standard, verification of features that could aid in discrimination between these mechanisms in clinical practice and research depends...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
September 2022
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| In: |
Pain
Year: 2022, Volume: 163, Issue: 9, Pages: 1812-1828 |
| ISSN: | 1872-6623 |
| DOI: | 10.1097/j.pain.0000000000002577 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1097/j.pain.0000000000002577 Verlag, lizenzpflichtig, Volltext: https://journals.lww.com/pain/Fulltext/2022/09000/Features_and_methods_to_discriminate_between.19.aspx 
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| Author Notes: | Muath A. Shraim, Kathleen A. Sluka, Michele Sterling, Lars Arendt-Nielsen, Charles Argoff, Karl S. Bagraith, Ralf Baron, Helena Brisby, Daniel B. Carr, Ruth L. Chimenti, Carol A. Courtney, Michele Curatolo, Beth D. Darnall, Jon J. Ford, Thomas Graven-Nielsen, Melissa C. Kolski, Eva Kosek, Richard E. Liebano, Shannon L. Merkle, Romy Parker, Felipe J.J. Reis, Keith Smart, Rob J.E.M. Smeets, Peter Svensson, Bronwyn L. Thompson, Rolf-Detlef Treede, Takahiro Ushida, Owen D. Williamson, Paul W. Hodges |
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| 245 | 1 | 0 | |a Features and methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system |b a Delphi expert consensus study |c Muath A. Shraim, Kathleen A. Sluka, Michele Sterling, Lars Arendt-Nielsen, Charles Argoff, Karl S. Bagraith, Ralf Baron, Helena Brisby, Daniel B. Carr, Ruth L. Chimenti, Carol A. Courtney, Michele Curatolo, Beth D. Darnall, Jon J. Ford, Thomas Graven-Nielsen, Melissa C. Kolski, Eva Kosek, Richard E. Liebano, Shannon L. Merkle, Romy Parker, Felipe J.J. Reis, Keith Smart, Rob J.E.M. Smeets, Peter Svensson, Bronwyn L. Thompson, Rolf-Detlef Treede, Takahiro Ushida, Owen D. Williamson, Paul W. Hodges |
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| 520 | |a Classification of musculoskeletal pain based on underlying pain mechanisms (nociceptive, neuropathic, and nociplastic pain) is challenging. In the absence of a gold standard, verification of features that could aid in discrimination between these mechanisms in clinical practice and research depends on expert consensus. This Delphi expert consensus study aimed to: (1) identify features and assessment findings that are unique to a pain mechanism category or shared between no more than 2 categories and (2) develop a ranked list of candidate features that could potentially discriminate between pain mechanisms. A group of international experts were recruited based on their expertise in the field of pain. The Delphi process involved 2 rounds: round 1 assessed expert opinion on features that are unique to a pain mechanism category or shared between 2 (based on a 40% agreement threshold); and round 2 reviewed features that failed to reach consensus, evaluated additional features, and considered wording changes. Forty-nine international experts representing a wide range of disciplines participated. Consensus was reached for 196 of 292 features presented to the panel (clinical examination—134 features, quantitative sensory testing—34, imaging and diagnostic testing—14, and pain-type questionnaires—14). From the 196 features, consensus was reached for 76 features as unique to nociceptive (17), neuropathic (37), or nociplastic (22) pain mechanisms and 120 features as shared between pairs of pain mechanism categories (78 for neuropathic and nociplastic pain). This consensus study generated a list of potential candidate features that are likely to aid in discrimination between types of musculoskeletal pain. | ||
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| 700 | 1 | |a Reis, Felipe J. J. |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Smeets, Rob J. E. M. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Svensson, Peter |e VerfasserIn |4 aut | |
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