ReviewBest practice & research. Clinical rheumatology2026
Decoding pain in axial spondyloarthritis: Mechanisms, management, and future directions.
Review in Best practice & research. Clinical rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Axial spondyloarthritis (axSpA) is an immune-mediated inflammatory condition that is associated with significant disease burden. While the use of biologic and targeted synthetic disease modifying antirheumatic drugs have led to significant improvements in disease outcomes, a substantial proportion of patients continue to face challenges with pain despite effective control of their inflammatory state. Chronic pain is one of the leading causes of disability and remains an unmet research and clinical need in axSpA. Phenotyping pain is essential to understand the etiology of symptoms and to aid in selecting the appropriate management plan in axSpA. In this review, we aim to highlight the mechanisms of pain, its phenotypes, and novel assessment tools. We also review our approach to managing chronic pain in axSpA and discuss future directions in this field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.