The treatment landscape in axial spondyloarthritis—do we need new therapies?
Bibliographic record
Abstract
Despite major therapeutic advances, many patients with axial spondyloarthritis (axSpA) fail to achieve remission or even low disease activity with currently available advanced therapies. This highlights an urgent need to understand why treatment responses remain suboptimal and how management can be improved. Several mechanisms contribute to persistent symptoms. Misdiagnosis and overdiagnosis remain frequent, often due to nonspecific imaging findings being misattributed to axSpA. Among correctly diagnosed patients, a substantial proportion experience pain not directly driven by inflammation. Nociplastic pain-frequently associated with fatigue, anxiety, depression, and sleep disturbances-is the predominant mechanism, while neuropathic pain is less common. These patients do not benefit from escalation of anti-inflammatory therapy but instead require tailored approaches addressing nociplastic pain, including structured exercise, cognitive-behavioural therapy, and pharmacological agents. However, robust clinical trial data in axSpA populations are lacking, and studies evaluating these interventions as add-on strategies are urgently needed. A smaller but clinically important subset of patients demonstrates true treatment-refractory (TR) axSpA, with objective inflammation persisting despite multiple biologic or targeted synthetic disease-modifying antirheumatic drugs. Novel therapeutic approaches under investigation include dual-targeted therapy with complementary mechanisms, selective depletion of pathogenic T cells, modulation of the HLA-B27 immunopeptidome through endoplasmic reticulum aminopeptidase 1/2 inhibition, and blockade of macrophage migration inhibitory factor. The primary challenge in axSpA today-besides the problem of the correct diagnosis-is to optimise the use of existing therapies for pain not related to inflammation, while simultaneously developing innovative options for TR disease. Together, these efforts will determine whether remission can become an attainable goal for all patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".