Towards a tool to discriminate between pain mechanistic descriptors: expert ranking of clinical features and allocation of weights using a forced choice paradigm

Muath A Shraim; Michele Sterling; Kathleen A Sluka; Lars Arendt-Nielsen; Miroslav Backonja; Ralf Baron; Vivian Blechschmidt; Daniel J Clauw; Milton Cohen; Brooke K Coombes; Sigrid Elsenbruch; Ian Gilron; Richard E Harris; Lester E Jones; Eleni Kapreli; Fusao Kato; Eva Kosek; Victoria J Madden; Jo Nijs; Felipe J J Reis; Andrew C Rice; Matthias Ringkamp; Annina B Schmid; Anushka Irani; Christin Veasley; Paul W Hodges

doi:10.1097/j.pain.0000000000003979

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Towards a tool to discriminate between pain mechanistic descriptors: expert ranking of clinical features and allocation of weights using a forced choice paradigm

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Towards a tool to discriminate between pain mechanistic descriptors: expert ranking of clinical features and allocation of weights using a forced choice paradigm

Muath A Shraim, Michele Sterling, Kathleen A Sluka, Lars Arendt-Nielsen, Miroslav Backonja, Ralf Baron, Vivian Blechschmidt, Daniel J Clauw, Milton Cohen, Brooke K Coombes, …

Pain (Amsterdam)

04/29/2026

DOI: 10.1097/j.pain.0000000000003979

PMID: 42048572

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Abstract

Pain treatments have modest effects. Health outcomes might be improved if treatments are matched to mechanisms underlying the persistence and biopsychosocial impact of an individual's pain. The International Association for the Study of Pain (IASP) defines 3 mechanistic pain descriptors presumed to involve different mechanisms-nociceptive, neuropathic, and nociplastic. Although treatments to address each descriptor have been proposed, there is no consensus on how to assign descriptors to clinical presentations. A recent consensus identified candidate clinical features to discriminate between descriptors in clinical practice and research. These need refinement to progress towards a tool. This study aimed to determine the rank and relative weight of identified clinical features to aid discrimination between mechanistic pain descriptors. Candidate clinical features (n = 196) were refined by the IASP Terminology Task Force to converge similar and remove redundant features. The Task Force (n = 24) and an expert panel (n = 39) ranked features using 1000minds conjoint analysis software and assigned weights based on discrete pairwise choices. Participants nominated from pairs of scenarios, which most likely indicated that pain aligned predominantly to a descriptor. Highest ranked features for neuropathic and nociplastic pain were aligned with IASP Clinical Criteria. Criteria for nociceptive pain have not been established. A ranked list of features shared by 2 mechanisms (indicating mixed mechanisms) was also identified. This study identified expert consensus on the highest ranked clinical features with potential to discriminate between pain descriptors, reflective of different underlying mechanisms. This study extends current frameworks by identifying and refining key discriminators for future operationalisation and validation.

Pain

Nociceptive

Nociplastic

Mechanism-based classification

IASP

Consensus

Neuropathic

Details

Title: Subtitle: Towards a tool to discriminate between pain mechanistic descriptors: expert ranking of clinical features and allocation of weights using a forced choice paradigm
Creators: Muath A Shraim - The University of Queensland
Michele Sterling - The University of Queensland
Kathleen A Sluka - University of Iowa
Lars Arendt-Nielsen - Aalborg University Hospital
Miroslav Backonja - National Institutes of Health
Ralf Baron - University Hospital Schleswig-Holstein
Vivian Blechschmidt - University of Mannheim
Daniel J Clauw - University of Michigan
Milton Cohen
Brooke K Coombes - The University of Queensland
Sigrid Elsenbruch - Ruhr University Bochum
Ian Gilron - Queen's University
Richard E Harris - University of California, Irvine
Lester E Jones - Singapore Institute of Technology
Eleni Kapreli - University of Thessaly
Fusao Kato - Jikei University School of Medicine
Eva Kosek - Science for Life Laboratory
Victoria J Madden - University of Cape Town
Jo Nijs - Vrije Universiteit Brussel
Felipe J J Reis - Pain in Motion
Andrew C Rice - Chelsea and Westminster Hospital
Matthias Ringkamp - Johns Hopkins University
Annina B Schmid - John Radcliffe Hospital
Anushka Irani - Mayo Clinic
Christin Veasley - Chronic Pain Research Alliance, Milwaukee, WI, United States
Paul W Hodges - The University of Queensland
Resource Type: Journal article
Publication Details: Pain (Amsterdam)
DOI: 10.1097/j.pain.0000000000003979
PMID: 42048572
ISSN: 1872-6623
eISSN: 1872-6623
Grant note: 1194937 / National Health and Medical Research Council UH3 AR076387 / NIAMS NIH HHS 2017405 / National Health and Medical Research Council R01 AR073187 / NIAMS NIH HHS 1091302 / National Health and Medical Research Council U24 NS112873 / NINDS NIH HHS 2027473 / National Health and Medical Research Council 1102905 / National Health and Medical Research Council
Language: English
Electronic publication date: 04/29/2026
Academic Unit: Iowa Neuroscience Institute; Nursing; Physical Therapy and Rehabilitation Science; Neuroscience and Pharmacology
Record Identifier: 9985157600702771

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