Management of Axial Disease in Patients With Psoriatic Arthritis: An Updated Literature Review Informing the 2021 GRAPPA Treatment Recommendations
Bibliographic record
Abstract
OBJECTIVE: Axial involvement in patients with psoriatic arthritis (PsA) is a common subset of this condition, but a unanimous definition has yet to be established. It has been defined by using different criteria, ranging from the presence of at least unilateral grade 2 sacroiliitis to those used for ankylosing spondylitis (AS), or simply the presence of inflammatory low back pain (IBP). Our aim was to identify and evaluate the efficacy of therapeutic interventions for treatment of axial disease in PsA. METHODS: This systematic review is an update of the axial PsA (axPsA) domain of the treatment recommendations project by the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). RESULTS: The systematic review of the literature showed that new biologic and targeted synthetic disease-modifying antirheumatic drug classes, namely interleukin (IL)-17A and Janus kinase inhibitors, could be considered for the treatment of axPsA. This would be in addition to previously recommended treatments such as nonsteroidal antiinflammatory drugs, physiotherapy, simple analgesia, and tumor necrosis factor inhibitors. Conflicting evidence still remains regarding the use of IL-12/23 and IL-23 inhibitors. CONCLUSION: Further studies are needed for a better understanding of the treatment of axPsA, as well as validated outcome measures.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".