Association between serine proteinase activity and response to intra-articular corticosteroid injections in psoriatic arthritis
Bibliographic record
Abstract
Objective: Intra-articular corticosteroid (IAS) injections are often used for the immediate relief of pain and inflammation in the joint of psoriatic arthritis (PsA) patients. However, studies identifying factors that can predict response to IAS injections are lacking. We aimed to assess the usefulness of serine proteinase activity measurements in PsA synovial fluid (SF) samples obtained at the time of injection in predicting clinical response. Methods: PsA patients with available SF samples from the knee joint were identified from the University of Toronto PsA cohort. Clinical response was defined as an absence of tenderness or swelling in the injected joint at the first post-injection visit, at either 3 or 6 months. Proteinase activity was determined by measuring cleavage of fluorogenic tri-peptide substrates for trypsin-like (VPR-AMC and VLK-AMC) and chymotrypsin-like (AAPF-AMC) serine proteinases. Generalized estimating equation (GEE) models were used to investigate which factors were associated with response. Results: A total of 32 patients with 60 injected joints and data available for follow-up at 3 or 6 months were included in the analysis, with 25 (41.7%) injected joints resulting in clinical response. Age, sex, active joint count, medications and serine proteinase activity at the time of injection were included as covariates. Only treatment with biologics was significantly associated with response at 3 or 6 months in the multivariate reduced model (OR 3.02, p = 0.027). Conclusion: We could not demonstrate an association between SF serine proteinase activity and response to IAS injection. Biologic agents significantly improve the likelihood of achieving clinical response.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".