Hidden in plain sight: Is there a crucial role for enthesitis assessment in the treatment and monitoring of axial spondyloarthritis?
Bibliographic record
Abstract
OBJECTIVE: To review the evidence surrounding the pathophysiology of enthesitis in axial spondyloarthritis (axSpA), its prevalence and contribution to the overall disease burden, and response to treatment at axial and peripheral sites. METHODS: Literature searches of the Cochrane Library, PubMed, and Embase / Medline using the terms "enthesitis", "enthesopathy", "spondyloarthritis", "axial spondyloarthritis", and "ankylosing spondylitis" were conducted. Publications mentioning enthesitis or enthesopathy in the context of pathophysiology, diagnosis, or treatment were included. RESULTS: Enthesitis is a common symptom of axSpA, occurring with high prevalence at axial and several peripheral sites. Inflammation at the site of enthesis is an early key manifestation of axSpA. Clinically evaluable enthesitis contributes significantly to the burden of disease, correlating with worse symptomatology and downstream structural damage. Despite its importance in driving axSpA disease processes, enthesitis is somewhat neglected in current approaches to disease assessment and management. Enthesitis is excluded from some commonly used disease activity measures, is not routinely assessed in clinical practice, and many methods of clinical assessment omit key accessible axial sites, such as the spinous processes. CONCLUSION: Enthesitis plays a central role in driving the pathophysiology of axSpA. There is a need for a renewed focus on the early detection, measurement and treatment of enthesitis.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 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".