Nociplastic pain in axial spondyloarthritis and psoriatic arthritis: role of JAK kinases in immunopathology and therapeutic impact of JAK inhibitors
Bibliographic record
Abstract
INTRODUCTION: Pain in both peripheral and axial joints is a major symptom in patients with psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA). Emerging evidence demonstrates pain mechanisms, beyond those related to inflammation or joint damage, based on aberrant processing of nociceptive stimuli peripherally as well as centrally. The Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway has been implicated in the processing of pain beyond its role in mediating inflammation and inhibitors of this pathway approved for the treatment of axSpA and PsA have been shown to alleviate a broad array of pain outcomes in both axial and peripheral joints. AREAS COVERED: We review recent definitions and standardization of the nomenclature for categorizing chronic pain according to causality, assessment tools to evaluate nociplastic pain, the pathophysiologic role of JAK-STAT signaling in nociplastic pain, evidence for the presence of nociplastic pain in axSpA and PsA, and the impact of JAK inhibitors (JAKi) on pain outcomes in clinical trials (PubMed: 01/01/2019-04/01-2024). EXPERT OPINION: Nociplastic pain assessment has been confined almost entirely to the use of a limited number of questionnaires in cross-sectional studies of these diseases. Though effective for alleviating pain, it is unclear if JAKi specifically impact nociplastic pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".