Effectiveness of Disease-Modifying Antirheumatic Drugs for Enthesitis in a Prospective Longitudinal Psoriatic Arthritis Cohort
Bibliographic record
Abstract
Objective Our objective was to assess the effectiveness of conventional and targeted disease-modifying antirheumatic drugs (cDMARDs and tDMARDs, respectively) in treating enthesitis in psoriatic arthritis (PsA). Methods Patients with active enthesitis, defined as ≥ 1 tender entheses (of the 29 enthesis sites included in the Spondyloarthritis Research Consortium of Canada Enthesitis Index, the Leeds Enthesitis Index, and the Maastricht Ankylosing Spondylitis Enthesitis Score), who were enrolled in a large PsA cohort were included. Medications at baseline were classified into 3 mutually exclusive categories: (1) no treatment or nonsteroidal antiinflammatory drugs (NSAIDs) only; (2) cDMARDs ± NSAIDs; and (3) tDMARDs ± cDMARDs/NSAIDs. Complete resolution of enthesitis (no tender enthesis) at 12 months was the primary outcome. Logistic regression models were developed to determine the association between medication category and enthesitis resolution. Results Of the 1270 patients studied, 628 (49.44%) had enthesitis. Of these, 526 patients (51.71% males; mean [SD] age 49.02 [13.12] years; mean enthesitis score 2.13 [2.16]; median enthesitis score 2 [IQR 1-2]), with adequate follow-up were analyzed. Complete resolution of enthesitis was noted in 453 (86.12%) patients, within a mean period of 8.73 (3.48) months from baseline. In the regression analysis, though not significant, DMARDs (categories II and III) had higher odds ratios (ORs) compared to category 1 for resolution of enthesitis. Enthesitis resolution was associated with lower joint activity (OR 0.97, 95% CI 0.95-0.99;P= 0.01) and male sex (OR 1.66, 95% CI 0.97-2.84;P= 0.06). Conclusion Resolution of enthesitis was observed in 86% of patients in an observational setting regardless of the medication used. Future effectiveness studies may warrant evaluation of enthesitis using advanced imaging.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".