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Cited by 409

The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.

Assessment of Chronic Pain: Domains, Methods, and Mechanisms

Open access
Fillingim, Roger B., Loeser, John D., Baron, Ralf, Edwards, Robert R.·2016·The Journal of Pain

Abstract

Accurate classification of chronic pain conditions requires reliable and valid pain assessment. Moreover, pain assessment serves several additional functions, including documenting severity of the pain condition, tracking the longitudinal course of pain, and providing mechanistic information. Thorough pain assessment must address multiple domains of pain, including the sensory and affective qualities of pain, temporal dimensions of pain, and the location and bodily distribution of pain. Where possible, pain assessment should also incorporate methods to identify pathophysiological mechanisms underlying the pain. This article discusses assessment of chronic pain, including approaches available for assessing multiple pain domains and for addressing pathophysiological mechanisms. We conclude with recommendations for optimal pain assessment.

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Registry

Crossref · 10.1016/j.jpain.2015.08.010

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Metadata

Authors
Fillingim, R. B., Loeser, J. D., Baron, R., & Edwards, R. R.
Venue
The Journal of Pain
Publisher
Elsevier BV
Published
2016-09
Volume
17
Issue
9
Pages
T10-T20
ISSN
1526-5900 (ISSN-L)
OpenAlex
W2515214979
Cited by
409
References
93
FWCI
11.80
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Assessment of Chronic Pain: Domains, Methods, and Mechanisms. https://app.cochat.ai/papers/10.1016/j.jpain.2015.08.010
The paper
Fillingim, R. B., Loeser, J. D., Baron, R., & Edwards, R. R. (2016). Assessment of Chronic Pain: Domains, Methods, and Mechanisms. The Journal of Pain. https://doi.org/10.1016/j.jpain.2015.08.010

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.